power consumption with $P_{\textrm{dyn}}=27$ dBm and $P_{\textrm{sta}}=1$ mW.
There is a 1 bit/s/Hz minimum rate threshold for each user. RS achieves a
larger EE than NOMA and SDMA in both subfigures. Again, NOMA achieves the EE
worst performance when there is no channel strength disparity while it
slightly outperforms SDMA when there is a 10 dB channel strength disparity and
low transmit power constraint.

(a) $\sigma_{1}^{2}=1,\sigma_{2}^{2}=1$

(b) $\sigma_{1}^{2}=1,\sigma_{2}^{2}=0.09$

Figure 24: Energy efficiency versus transmit power budget comparison in MISO
BC with perfect CSIT.

Fig. 22–24 show the SE and EE gains of RSMA in the downlink for different user
deployments and CSIT conditions. In the uplink, the authors in [65] have also
shown that RSMA achieves a higher spectral efficiency than NOMA and other OMA
strategies, as illustrated in Fig. 25 thanks to its capability of achieving
each point at the boundary of the capacity region. In contrast, NOMA can only
reach the corner points at the boundary of the capacity region and time
sharing is needed to achieve the points along the line segment between the two
corner points.

Figure 25: Sum rate versus the transmit power budget of each user in SISO MAC
with perfect CSIT [65].

####  IV-D2 General and Unified

As widely acknowledged and discussed in the literature of RSMA [8, 24, 9, 29,
25], RSMA is a general and universal MA scheme, interference management
strategy, and non-orthogonal transmission framework that unifies SDMA, NOMA,
OMA, physical-layer multicasting and treats them as sub-schemes or special
cases. Such universality is reflected in the SE and EE gain over existing MA
schemes as per Fig. 22–25, as well as in its operational region illustrated in
Fig. 26. In Fig. 26, a specific channel realization
$\mathbf{h}_{1}=\frac{1}{\sqrt{2}}[1,1]^{H}$ and
$\mathbf{h}_{2}=\frac{\gamma}{\sqrt{2}}[1,e^{j\theta}]^{H}$ is considered. The
$x$-axis is
$\rho=1-\frac{|\mathbf{h}_{1}^{H}\mathbf{h}_{2}|^{2}}{\|\mathbf{h}_{1}\|^{2}\|\mathbf{h}_{2}\|^{2}}$,
which indicates the channel angle between the two users ($\rho=0$ and $\rho=1$
corresponding to aligned and orthogonal channel directions, respectively). The
$y$-axis is the channel disparity between the two users in dB, which is given
as $\gamma_{\mathrm{dB}}=20\log_{10}(\gamma)$. The parameters and the precoder
design are detailed in [24]. RS automatically reduces to the existing MA
schemes in some regimes while it outperforms all existing schemes in the
remaining regimes especially when the SNR is high. Therefore, instead of using
OMA, NOMA, SDMA and optimize them for each propagation deployment, one can use
a single and universal RS scheme in wireless communication networks.

(a) SNR$=10$ dB

(b) SNR$=30$ dB

Figure 26: Operation regions for RS, SDMA, NOMA, OMA and Multicast with
perfect CSIT [24].

####  IV-D3 Flexible

Fig. 27 illustrates the impact of network load to the system performance for a
MISO BC with $K=6$ users and imperfect CSIT with $\alpha=0.5$. The variances
of user channels are randomly selected from $[0.1,1]$. Two MISO NOMA schemes
are considered as baseline schemes, namely, MISO NOMA with a single user group
($G=1$) which is motivated by SC and SIC in SISO BC, and MISO NOMA with three
user groups ($G=3$) which clusters 6 users into 3 user groups and inner-group
users are served by SC and SIC. Readers are referred to [7] for more details
on the parameter settings and baseline schemes of MISO NOMA. When the number
of transmit antennas is $M=3$, the network load is extremely overloaded. In
this case, MISO NOMA ($G=1$) achieves a better MMF rate than SDMA and MISO
NOMA ($G=3$). However, as the number of transmit antennas increases, the
performance of MISO NOMA ($G=1$) decreases due to a significant loss in DoF.
MU–LP outperforms the two MISO NOMA when the network is underloaded ($M=6$).
In all subfigures, 1-layer RS, which only uses a single layer of SIC at each
user, achieves the best MMF rate no matter the network is underloaded or
overloaded. As per Fig. 22–27, RSMA is a flexible MA scheme which is suited
for different user deployments (diverse channel directions and strength),
network loads (underloaded and overloaded), CSIT condition (perfect and
imperfect CSIT), and SNR regimes (low, medium, and high SNR regimes). The root
of such flexibility is the powerful interference management ability of RSMA
introduced by the common stream (vectors). By adjusting the sub-messages
encapsulated in each common stream as well as the power allocation, RSMA
dynamically alters the portion of interference to be pre-canceled at the
transmitter and decoded at the receivers.

(a) Overloaded $M=3$ (b) Overloaded $M=4$

(c) Overloaded $M=5$ (d) Underloaded $M=6$

Figure 27: Max-min rate vs. SNR comparison in MISO BC with imperfect CSIT,
$\alpha=0.5$, $K=6$, $\sigma_{k}^{2}\in[0.1,1]$ [7].

(a) $M=32,K=8,N=1,Q_{c}=Q_{p}=1$

(b) $M=64,K=8,N_{r}=4,Q_{c}=Q_{p}=3$

Figure 28: Throughput vs. user speed comparison in (massive) MIMO BC with
outdated CSIT employing OFDM and a 3GPP channel model, and $10$ ms CSI
feedback delay [66].

####  IV-D4 Robust

Robustness is one of the most important benefits of RSMA discovered in recent
years [19, 18, 67, 9]. The robustness of RSMA is grounded in deep information
theoretic results, where RSMA is shown to achieve the optimal DoF in MISO BC
when CSIT is imperfect [19]. Motivated by such discovery, different sources
that impair CSIT are studied with RSMA such as pilot contamination [68],
channel estimation errors [18], user mobility [66], etc. RSMA is shown a
significant SE gain over existing MA baselines for all sources of CSIT
impairments. Taking user mobility as an example in Fig. 28, we have shown in
[66] that RSMA achieves a significantly higher user speed (i.e., 40 km/h) than
SDMA (i.e., 5 km/h) for a given QoS rate constraint (i.e., 8 bps/Hz) thanks to
its powerful interference management ability.

####  IV-D5 Reliable and Low Latency

: As shown in [32, 34] for downlink and in [69] for uplink, RSMA is more
reliable than SDMA and NOMA under finite blocklength. This is illustrated in
Fig. 29 for underloaded and overloaded downlink settings where we note that
RSMA can achieve a given MMF rate at a lower blocklength, therefore enabling
lower latency communications, compared to other MA schemes. It is important to
recall here again that 1-layer RS scheme of RSMA is used in Fig. 29, and
therefore requires a single SIC. This is in contract with NOMA that achieves
lower performance at the expense of the need for 7 SIC layers at the receivers
in Fig. 29(b).

(a) Underloaded $M=4,K=2$ (b) Overloaded (M=4, K=8)

Figure 29: MMF rate versus blocklength (solid lines) of downlink SDMA (red),
NOMA (yellow), RSMA (blue) at SNR$=$20dB [34]. Dashed line is the upperbound
achieved with infinite blocklength.

###  IV-E Lessons Learned

• RSMA framework is a superset of OMA, SDMA and NOMA, and can specialize to
each of them depending on how messages are mapped to streams. This holds for
both uplink and downlink, as well as in SIMO, MISO and MIMO settings. • All
instances of RSMA are supersets of SDMA and OMA, but not all, such as 1-layer
RS scheme, are supersets of NOMA. This enables RSMA schemes with better
performance than NOMA at a much lower receiver complexity (e.g., only one SIC
layer). • RSMA can be linearly precoded or non-linearly precoded. • RSMA is
applicable to all major settings of a cellular network, namely downlink,
uplink, and multicell, for general MIMO deployments (with SISO, SIMO, MISO as
special cases). • RSMA is spectrally and energy efficient, general and
unified, flexible, robust, reliable and has lower latency.

##  V Numerous Applications for RSMA

Given its fundamental communication theoretic principles, and its unique
features (efficiency, universality, flexibility, robustness and resilience,
reliability and low latency), RSMA finds applications in all modern multi-user
scenarios. We here provide a description of over forty promising scenarios and
applications of RSMA and briefly explain how and why RSMA is
beneficial111111Other promising scenarios and applications are also discussed
in Section VII on frequently asked questions.. A subset of those scenarios and
applications is illustrated in Fig. 30. All those applications demonstrate the
suitability of RSMA for FeMBB, eURLLC, umMTC, and new wireless services in 6G
[29, 28]. Importantly, the benefits and applications of RSMA have not only
been explored from an academic perspective with the performance superiority
over other MA techniques confirmed via stochastic analysis [70, 71, 72, 73]
but have also been confirmed using realistic link-level simulations over 5G
compliant channel models [33, 74, 75, 35, 76, 77]. Interested readers are also
encouraged to consult [25, 26, 38, 39] for some more descriptions of
interesting applications and future works.

Figure 30: A subset of promising scenarios and applications of RSMA.

####  V-1 Downlink SISO

RSMA can be used for both degraded or non-degraded BC. In the degraded BC,
RSMA boils down to NOMA (since NOMA is capacity achieving for degraded BC) but
RSMA can be used to decrease the receiver complexity and still come close to
the capacity region with a reduced number of SIC layers compared to NOMA [8].
In the non-degraded BC, RSMA achieves a strictly larger rate region than NOMA
(see Remark 4).

####  V-2 Downlink MISO

RSMA can be used in various MISO settings with perfect and imperfect CSIT. The
design and precoder optimization of RSMA would depend on the objective value,
e.g., WSR, MMF, EE, and the modeling of the CSIT imperfection, e.g., unbounded
vs bounded error [18, 8, 48, 78, 64, 79, 80, 81]. One challenge with imperfect
CSIT is to make sure that the rate optimized are achievable. 1-layer RS is the
most common scheme but other schemes based on multi-layer RSMA can also be
used [8]. A strong benefit of RSMA is its inherent robustness to imperfect
CSIT.

####  V-3 Downlink MIMO

RSMA can be used in settings with multiple receive antennas at the users. MIMO
RSMA enables to transmit vectors of common and private streams, which requires
a special design [22, 82, 35, 83, 84, 85, 86]. Various CSIT/CSIR assumptions
and objective values can also be considered in the MIMO setting.

####  V-4 Uplink

Uplink RSMA avoids the need of time sharing to achieve the capacity region,
which finds new applications in modern systems subject to latency constraints
[23, 87, 69, 65, 88, 89]. Much attention has been brought to the uplink SISO
RSMA, but uplink multi-antenna RSMA is also possible as shown in Section IV-B.

####  V-5 Unifying OMA, SDMA, NOMA, Multicasting

RSMA is universal and general in the sense that it encompasses many different
schemes (such as OMA, SDMA, NOMA, physical-layer multicasting) as particular
instances [24, 8, 90, 7]. It is interesting to study and understand under what
propagation conditions, e.g., disparity of channel strengths and angle between
user channels, RSMA boils down to each of those schemes [24].

####  V-6 Statistical CSIT

RSMA is helpful in scenarios where only statistical CSIT (i.e., the
distribution information of the user channels) is available, This is a
particular instance of imperfect CSIT, but important in practice as it leads
to low feedback overhead. This can be for instance a channel covariance matrix
calculated by averaging over frequency and time resources and reported once in
a while to the base station. RSMA has been found robust to scenarios where
only statistical CSIT is available [91, 58, 92, 93]

####  V-7 Quantized Feedback

Quantized feedback is another popular CSI feedback mechanism that relies on a
codebook to quantize the channel. Due to quantization error, the CSIT is
imperfect and RSMA has been found robust to the quantization error [47, 94].
Interestingly the number of feedback bits needed to achieve a given sum-rate
performance is decreased with RSMA compared to conventional SDMA/MU-MIMO,
therefore enabling an overhead reduction [47, 95].

####  V-8 Imperfect CSIT and CSIR

Aside imperfect CSIT, for which we know RSMA is robust, imperfect CSIR is also
an important problem that has received less attention [96]. The rate
achievability and the SIC error propagation are two important issues to
consider when studying RSMA with imperfect CSIR.

####  V-9 Frequency Division Duplex (FDD) Massive MIMO

One popular strategy in FDD massive MIMO is to rely on two-tier precoders
where the first tier precoder is based on channel statistics to cancel inter-
cluster interference and the second tier based on instantaneous CSI feedback
to manage intra-cluster interference [97, 98]. Unfortunately if the space
spanned by the covariance matrices of the clusters overlap or if the channel
statistics and/or the CSIT is imperfect, the performance of such approaches
degrade and RSMA can be used to mitigate those issues [58].

####  V-10 Time Division Duplex (TDD) Massive MIMO

TDD massive MIMO also has its impairments such as pilot contamination due to
multiple users sharing the same121212In massive machine-type communications,
due to the large number of devices, their sporadic access behaviour and
limited coherence interval, active devices have a higher chances to utilize
the same pilot for uplink channel estimation. pilot sequence when performing
uplink channel sounding and channel aging due to mobility and latency between
the channel sounding phase and the data transmission phase. Those two issues
effectively lead to imperfect CSIT that need to be tackled. RSMA has been
shown robust to mitigate both problems [66, 99, 68]

####  V-11 Hardware Impairments

Another impairment in Massive MIMO comes from the hardware, e.g., phase noise,
digital-to-analogue (DAC) and analogue-to-digital (ADC) converters. Phase
noise can effectively lead to a scenario where the CSIT is imperfect and RSMA
has been shown to be robust against hardware impairments such as phase noise
[100]. Quantization errors due to finite resolution DAC and ADC also lead to
multi-user interference that can be efficiently and flexibly managed using
RSMA [101, 102, 103].

####  V-12 Cell-Free Massive MIMO

Cell-free refers to distributed antennas systems, which is subject to the same
pilot contamination problem as conventional TDD massive MIMO. The disparity of
path loss nevertheless makes the analysis and performance gap between schemes
different. RSMA was found robust to pilot contamination in various cell-free
topologies [104].

####  V-13 Millimeter Wave and TeraHertz Systems

Higher frequency bands are subject to their own set of challenges such as high
path loss, blocking and expensive RF chains. RSMA can be used and designed in
conjunction with hybrid analogue-digital precoding to outperform conventional
SDMA strategies at high frequencies [91, 105, 106]. RSMA in combination with
cooperative communications can also be used to further combat the path loss
and blocking issues [107].

####  V-14 Non-Linear Precoding and DPC

RSMA is commonly designed using linear precoding due to its practical use in
real systems. Nevertheless, non-linear precoding could also be used to precode
the private streams, using for instance DPC, Tomlinson-Harashima precoding,
vector perturbation, etc. Recent works have demonstrated the benefits of non-
linear precoded RSMA [108, 9, 109, 110]. The combination of RSMA and DPC (so-
called DPCRS strategy as per Fig. 18 and Fig. 19) is particularly suited to
imperfect CSIT settings since we know that DPC is capacity achieving with
perfect CSIT. As the CSIT quality improves, the power allocation to the common
stream decreases, and DPCRS progressively boils down to conventional DPC for
multi-antenna BC. It is of interest to investigate how we could further
enhance the performance in imperfect CSIT beyond that achieved by DPCRS
strategy [9].

####  V-15 Cooperative and Relaying Systems

Cooperative communications and RSMA, i.e., cooperative RSMA as in Fig. 10,
form a happy marriage as shown in [52, 53] since a user can decode the common
stream and its private stream and forward the common stream to help improving
the decodability of the common stream at a cell edge user. By doing so, RSMA
can efficiently cope with a wide range of propagation conditions (disparity of
user channel strengths and directions), compensate for the performance
degradation due to large path loss, extend the coverage, and outperforms SDMA,
NOMA, and cooperative NOMA. Those observations also hold in the imperfect CSIT
setting [111]. RSMA can also find other interesting applications in multi-user
relaying systems [112, 113, 114, 115]. It would be worth further exploring
how, in the classical relay channel [116], as well as the cooperative
communication works [117, 118], RS inherently plays a role in the decode-and-
forward protocol and its variations.

####  V-16 Full Duplex

Self interference is a major problem in full duplex systems. Thanks to its
flexible interference management ability, RSMA can increase the range of self-
interference over which full duplex outperforms half duplex [119].

####  V-17 Physical Layer Security

Security at the physical layer has attracted a lot of attention in the past
decade [120]. Two interesting scenarios occur for RSMA: first, the
eavesdropper is not one of the users served by RSMA (hence, the eavesdropper
is not the intended recipient of any message transmitted by the transmitter,
but only intercepts confidential messages sent to other authorized users), and
second, the eavesdropper is one of the users and can therefore decode the
common stream [121, 122]. In the former case, the common stream can be used to
manage the interference between users and therefore increase the sum-rate of
those users but also to act as an artificial noise (AN) to confuse the
eavesdroppers. In the latter case, an eavesdropper could decode a common
stream and could therefore reconstruct the original message if it can decode
the corresponding private. This leads to a secrecy constraint for the private
stream and a tradeoff between sum-rate and secrecy rate [123, 124, 125]. RSMA
achieves better WSR and is more robust to channel errors than SDMA while
ensuring all users’ security requirements. In contrast, because the entirety
of the message of a user is mapped to the common stream in NOMA, NOMA cannot
guarantee all users’ secrecy rate constraints [123]. Other interesting
analysis of RSMA in the presence of untrusted users has recently appeared in
[126].

####  V-18 Energy Efficient Networks

EE is an increasingly important metric. In general, the superiority of RSMA in
terms of spectral efficiency translates into an EE gain over conventional MA
baselines such as OMA, SDMA and NOMA [63, 127]. Nevertheless, SE maximization
and EE maximization are two conflicting objectives in the moderate and high
SNR regimes. This calls for the study of the tradeoff between the two
criteria. In [128], the performance of RSMA is shown to be superior to or
equal to SDMA and NOMA in terms of SE, EE, and their tradeoff.

####  V-19 Reconfigurable Intelligent Surfaces (RIS)

RIS is equipped with a large number of passive elements placed in the
environment that can be adjusted so as to provide a passive beamforming gain
and make the channel propagation more favourable. The interplay between RIS
and RSMA is very attractive and has been shown very promising by several works
[129, 130, 131, 39, 132, 133, 134, 135, 136, 137, 138]. The advantages of RIS-
aided RSMA are higher spectral efficiency, coverage extension and beam control
flexibility thanks to the presence of the common stream, robustness to CSI
imperfection which is welcome given the channel acquisition challenge in RIS,
and lower computational and hardware complexity because RSMA superiority means
RSMA can afford operating with less complex RIS architectures while
maintaining the same overall performance [39].

####  V-20 Multi-Cell Networks

The concept of RS for the 2-user IC can be extended to more than 2 users, or
to multiple cells in cellular network context [139]. Furthermore each node in
each cell can be equipped with multiple transmit antennas and precoders
jointly with the message split need to be optimized across all cells to
maximize the objective function accounting for any potential imperfection in
the CSIT [140, 59, 22, 141, 60, 142]. Of particular interest, [59] showed that
in a multi-cell multi-antenna setting with imperfect CSIT, the transmitters
should adopt a so called topological RS (TRS) strategy that consists in a
multi-layer structure and in transmitting multiple common messages to be
decoded by groups of users rather than all users. Though TRS was studied from
a communication and information theoretic point of view, it remains to be
studied how TRS could be optimized using optimization tools so as to maximize
its performance at finite SNR.

####  V-21 Coordinated Multi-Point (CoMP)

RSMA can be used for CoMP joint transmission where all base stations
collaborate by sharing CSI and data. In such a setting, all antennas at the
base station act together to form a giant BC or MAC (depending on whether the
focus is on downlink or uplink) and precoders and power across streams can be
optimized as a function of the CSI and path loss disparity subject to the per-
cell power constraints. It was shown in [61] that whenever there is little
inter-user channel strength disparity but large inter-cell channel disparity,
SDMA was a suitable option. On the other hand, whenever there is a large
inter-user channel strength disparity but little inter-cell channel disparity,
NOMA was a better option. In comparison, RSMA always bridges, generalizes, and
outperforms existing SDMA and NOMA strategies. It was shown to be suited to
any deployment with any inter-user and inter-cell channel disparities.
Applications of RSMA to uplink CoMP would be worth investigating.

####  V-22 Cloud and Fog-Radio Access Network (C/F-RAN)

C-RAN consists of multiple remote radio heads (RRH) connected through
fronthaul links to a baseband unit that performs central processing. CoMP
joint transmission can be implemented across all those RRH while accounting
for the challenging feature that the fronthaul links have a limited capacity.
Various RSMA approaches have been proposed for C-RAN: 1) RSMA transmit signal
is compressed before being transmitted over the fronthaul [143]; 2) the common
and private streams of RSMA to be transmitted over each fronthaul are wisely
selected so as to satisfy the fronthaul constraints [144, 145, 146, 147, 148,
149, 62, 150, 151, 152]. For a given fronthaul capacity constraint, both
approaches have demosntrated that RSMA is more spectrally and energy efficient
than SDMA and NOMA.

####  V-23 Dynamic Resource Management and Cross-Layer Optimization

In RSMA-aided networks, network resource management has received more and more
attention. Many of these works, however, only consider the short-term
optimization of system resources without taking into account the long-term
network operation constraints and objectives. Furthermore, they do not
consider random traffic arrivals. To meet the explosive access demands of
mobile devices, the multicast communication of a satellite and aerial-
integrated network with RSMA is studied in [153]. Specifically, the unmanned
aerial vehicle (UAV) sub-network uses RSMA to support massive access of
internet of things (IoT) devices in a content delivery scenario. However, in
practice, users may require different types of data traffic, such as high-
quality video streaming, voice, video phone, online games, network
broadcasting, and so on. This necessitates the cross-layer design of RSMA-
aided system to cope with traffic exposure rate and long-term system
constraints. It is recommended that joint adaptive source encoding and cross-
layer resource management schemes be studied. For example, using a supervised
learning-based approach for cross-layer resource allocation in a NOMA-aided
system [154], the execution time can be reduced by 98% while ensuring that
each user has at least one subcarrier. Given the outstanding characteristics
of RSMA, more cross-layer (across physical - PHY, medium access control - MAC,
and application - APPL - layers) dynamic resource management solutions and
optimizations are expected to bring further improvement to the system
performance. Though most of the RSMA optimization works focus on PHY and MAC
layers (beamforming and power allocation, time and frequency resource
allocation), some works apply RSMA for the wireless streaming of video and
consider joint optimization across PHY, MAC, and APPL (hence, with the
optimization of the encoding rate adaptation for video) [155]. This is an
important research direction for RSMA, especially for mobile internet (where
video takes more than 70% of traffic) and emerging applications such as 360
video, autonomous driving, metaverse for 6G and beyond.

####  V-24 Wireless Caching

The small storage capacity of edge devices combined with the high energy
consumption of active caching shorten the standby period of user devices. By
positioning caching devices adjacent to the user terminal, wireless caching
networks (WCN) can reduce recurrent file transfers to achieve low power
consumption caching. Furthermore, the spectrum efficiency can be increased by
combining RSMA with WCN. Two caching policies can be examined, i.e., the most
popular content (MPC) and the intelligent coded caching (CC) policy. In MPC
policy, multiple popular files can be superimposed in the power domain to form
a mixed file based on RSMA, which is broadcasted to cache devices by the base
station [156, 157, 158]. Another approach of cache placement is the coded
caching (CC), where partitions of the files are stored instead [159]. Multiple
cache-enabled receivers can be served using both caching policies within the
help of RSMA [156, 157, 158]. It is shown that the caching gains can be
improved significantly, the mutual benefit can be achieved through
collaborative design of caching placements and RSMA [156, 157, 158]. In a way,
RSMA for caching-aided multi-antenna BC expands upon, and inherits the main
features of, RSMA used for the classical multi-antenna BC with imperfect CSIT.
Additionally, using RSMA, the caching device can provide services to numerous
users in the same time-frequency resource block. The quantity of caching
files, their level of popularity, and their network resources all affect the
key indicator of wireless caching networks, e.g., the system hit probability.

####  V-25 Overloaded Cellular Internet of Things and Massive Access

Cellular networks will have to cope with extensive IoT devices, and
consequently serve simultaneously a large number of devices with heterogeneous
demands and CSIT qualities. In [160, 161], RSMA is used to tackle such a
scenario by considering an overloaded MISO downlink with two groups of CSIT
qualities, namely, one group of users (representative of high-end devices) for
which the transmitter has partial knowledge of the CSI, the other group of
users (representative of IoT devices) for which the transmitter only has
knowledge of the statistical CSI. RSMA is shown to be DoF-optimal in such a
setting with heterogeneous CSIT qualities, more efficient than various MA
baselines, robust to CSIT inaccuracy, and flexible to cope with heterogeneous
QoS rate constraints of all high-end and low-end users.

####  V-26 Joint Communication and Jamming

Thanks to its flexibility and robustness, RSMA can be used to efficiently
communicate to information users (IUs) and simultaneously jam adversarial
users (AUs) to disrupt their communications [162, 163]. The precoders and
power allocation to common and private streams can be optimized based on
imperfect CSIT for IUs and statistical CSIT for AUs to maximize the sum-rate
under jamming power constraints on the pilot subcarriers of AUs (as jamming
pilot subcarriers is known to be very effective disruptive method). RSMA is
shown to outperform significantly conventional MA schemes.

####  V-27 Non-Orthogonal Unicast and Multicast (NOUM)

NOUM refers to mixed traffic services where a multicast (genuinely intended to
multiple users, i.e., not user-specific) message is transmitted to multiple
users and additionally one unicast (user-specific) message is transmitted per
user. Conventionally OMA is used to transmit unicast and multicast services on
different resources. This is suboptimal and a better approach is to superpose
the multicast message on top of the unicast messages and use one SIC at each
receiver to decode the multicast message first and then the intended unicast
message. RSMA can do better by making a more efficient use of the SIC. Indeed,
by splitting the unicast message into common and private parts and encoding
jointly the common parts and the multicast message into a common stream to be
decoded by all users, the SIC can be efficiently exploited for the dual
purpose of separating multicast from unicast but also better manage
interference between unicast streams [164, 127, 165, 166]. Taking the 2-user
architecture of Fig. 7 as an example, RSMA in NOUM is obtained by encoding a
multicast message $W_{0}$ (genuinely intended to both users) along with common
parts $W_{\mathrm{c},1}$ and $W_{\mathrm{c},2}$ into a common stream. User-$k$
then decodes the common stream to retrieve $W_{0}$ and $W_{\mathrm{c},k}$,
before decoding its private stream. RSMA has been shown to outperform SDMA and
NOMA counterparts in NOUM [164, 127, 165].

####  V-28 Multigroup Multicast

This scenario considers $K$ users grouped into $G<K$ groups and a transmitter
that delivers on the downlink one multicast message per group, i.e., all users
in the same group are interested in the same multicast message. Conventional
BC is a subset of that setting where there is only one user per group. Such a
scenario can occur in broadcasting services, caching settings, satellite
communications, multi-view video, virtual reality (360) video, online gaming,
metaverse, etc. The challenge of multigroup multicast is that the number of
users is often large compared to the number of transmit antennas, i.e.,
overloaded network, which creates severe multigroup interference issues. RSMA,
thanks to its flexibility, is able to tackle that multigroup interference
efficiently and outperform both SDMA and NOMA schemes significantly [67, 167,
168, 169, 170, 171, 172] and cope with various other multicast scenarios
relevant to 6G [173].

####  V-29 Multibeam Satellite Communications

Multibeam satellite is often modeled by a multigroup multicast where each beam
can be thought of one group. This comes from the superframe/frame-based
precoding assumption of multibeam satellite communication which is that one
codeword encodes all users’ messages in one beam. This is used in practice to
increase the efficiency of the error correcting codes. Other challenges in
satellite systems include the frequency reuse across multiple spot beams
creating high levels of interference, the per-feed power constraints, the
satellite channel subject to line-of-sight and large path loss, the imperfect
CSIT and latency in CSI acquisition due to large round trip delay, the high
doppler for low earth orbit satellite, and the overloaded settings (many
terminals in each beam). RSMA has been found to be quite suited to tackle all
those challenges thanks to its efficiency, flexibility, and robustness [168,
174, 175, 176, 177].

####  V-30 Unmanned Aerial Vehicles (UAV)-Assisted Networks

Due to the great mobility and flexibility, UAVs are able to provide services
to users when they are outside of the base station’s coverage area or when the
user channel conditions are unfavorable. However, a UAV relies on its limited
capacity battery to fly, hover and communicate, which leads to a limited
endurance. Fortunately, RSMA can reduce the communication energy consumption
of UAV. By acting as an aerial base station, the UAV can serve multiple ground
users simultaneously using RSMA [178, 179, 180, 181]. RSMA for UAV leads to
new joint cross-layer optimization problem of UAV location, transmit power
allocation, bandwidth, and RSMA, accounting for CSI availability and traffic.
RSMA and UAV locations can also be designed along with methods used for
predicting the cellular traffic according to the analysis of previous data,
therefore further saving transmit power consumption [182].

####  V-31 Space-Air-Ground/Satellite-Terrestrial Integrated Networks

In satellite-terrestrial (or in space-air-ground) integrated network, the
satellite sub-network shares the same RF band with the terrestrial sub-
network. A higher spectrum efficiency and throughput is achieved via dynamic
spectrum access sharing to enhance spectrum utilization. However, severe
interference in and between the sub-networks is induced by an aggressive
frequency reuse, which calls for the use of efficient multi-antenna [183] and
multiple access strategies [184, 185, 153]. In this context, RSMA has been
shown to exhibit significant performance gains compared with various
traditional transmission strategies such as SDMA and NOMA in various settings
where only CSI is shared among sub-networks (to enable coordination of
precoders) or where CSI and data are shared (to enable cooperation as in CoMP
joint transmission across all antennas of both sub-networks) [184].

####  V-32 Constructive Interference Exploitation/Symbol-Level Precoding

RSMA is conventionally studied with Gaussian inputs, but in practice finite
constellations need to be used. Constructive interference (CI), also called
symbol-level precoding, exploits the finite constellation such the information
symbols are used, along with the CSI, in order to exploit the multi-user
interference to increase the useful signal received power. In other words, CI
designs the transmit precoders such that the resulting interference is
constructive to the desired symbol, i.e., the interference signal pushes/moves
the received symbols away from the decision thresholds of the constellation
towards the direction of the desired symbol. Interestingly, RSMA and CI
techniques can be combined to further enhance the sum-rate achieved by RSMA
with finite input alphabet [186, 187].

####  V-33 Reliability and Low Latency

There are many reliability and latency-sensitive applications, such as
industrial automation, smart grid and intelligent transportation. In order to
reduce the transmission latency, shortpackets with finite blocklength codes
are typically adopted. This brings a stringent latency requirement to the
physical layer. Interestingly, the efficiency, robustness, flexibility
benefits of RSMA over SDMA and NOMA in the infinite blocklength regime were
also confirmed in the finite blocklength regime for both downlink and uplink,
offering therefore the additional reliability and the low latency needed to
enable those applications [32, 34, 69]. In other words, RSMA can achieve a
given performance requirement with a smaller blocklength than that needed by
SDMA and NOMA. The significant performance gains of RSMA over SDMA and NOMA
were also confirmed in link-level evaluations with practical codes and finite
blocklengths [33, 75].

####  V-34 Integrated Radar Sensing and Communications

Integrated (radar) sensing and communications (ISAC) merges wireless
communications and remote sensing into a single system, where both
functionalities are combined via shared use of the spectrum, the hardware
platform, and a joint signal processing framework. It also enables sensing
capabilities of the network to help communications and inversely. The
challenge is that a transmitter not only has to serve multiple users
simultaneously but also has to satisfy radar performance requirements, lwhich
leads to a tradeoff between communication and radar performance and calls for
agile and versatile MA schemes. The flexibility and robustness of RSMA becomes
particularly handy in this setting as it was shown that RSMA can provide a
better communication-radar tradeoff than SDMA and NOMA schemes for a wide
range of propagation conditions and radar metrics [188, 189, 190, 76, 101,
102, 38, 191].

####  V-35 Grant-based, Grant-Free and Semi-Grant Free Transmission and
Massive Random Access

Grant-based transmission has been a conventional approach to access the
network when the network load is small. With the emergence of IoT devices and
mMTC, hybrid grant-based (GB) and grant-free (GF) transmissions are needed to
reduce latency. Grant-free access however leads to collisions that need to be
managed. In such scenario, GF users meet opportunities to share wireless
resources with GB users. In [192], RSMA was used in this setting where the GF
users split their messages to realize distributed contentions and utilize
transmit power most effectively for robust transmissions, meanwhile keeping
themselves transparent to the GB user. RSMA was shown to significantly
decrease outage probability and achieve full multi-user diversity gain without
restricting the GB and GF users’ target rates.

####  V-36 Network Slicing

To guarantee the performance of heterogeneous services involving FeMBB,
eURLLC, umMTC in 6G, network slicing is needed to allocate resources to
different services. Network slicing can be done in an OMA fashion, which means
that different services are isolated and allocated orthogonal (non-
interfering) resources. However, as the number of users grows, OMA-based
slicing may not be the optimal scheme for all scenarios, and a non-orthogonal
scheme may achieve a better performance. Thanks to its message splitting and
corresponding flexibility, RSMA has emerged as a superior MA scheme for
network slicing, outperforming OMA and NOMA in many scenarios [193, 194].

####  V-37 Cognitive Radio

Cognitive radio may increase spectral efficiency through secondary spectrum
sharing / dynamic spectrum access, transmitting under the interference
temperature. Various setups can be considered where primary transmitter-
receiver links communicate simultaneously with a group of cognitive secondary
users that form one of classical multi-user channels such as MAC, IC, or BC
[195]. In the BC, RSMA can for instance be employed at the secondary
transmitter to communicate with secondary users while limiting the
interference to primary users [163, 196]. Since the primary link remains
oblivious to the secondary system operation in cognitive radio, another
potential benefit of RSMA is whether the primary system does not need to time-
share the channel with the secondary users, unlike the conventional spectral-
gap filling approaches [195].

####  V-38 Optical and Visible Light Wireless Communications

RSMA is commonly studied for RF communications, but it can also be applied to
other communication systems such as optical and visible light, though the
constraints from those signals and systems need to be captured in RSMA design
and optimization, e.g., visible light signals have peak and average optical
power constraints (limited for eye safety and practical illumination
requirement), are non-negative and real due to the intensity modulation and
direct detection technique [197, 198, 199]. Similarly to RF systems, RSMA
outperforms SDMA and NOMA in optical and visible light communications [197,
198, 199].

####  V-39 Multi-carrier

Frequency domain using multi-carrier transmission (e.g., OFDM/OFDMA) can be
combined with RSMA in the same way SDMA and OFDM/OFDMA work together in 4G and
5G, namely the spectrum is divided into subbands (made of contiguous or
distributed subcarriers) and multiple users are paired together on one or
multiple subbands using RSMA [200, 163, 162, 171]. Resource allocation,
including user pairing per subband and power allocation to common and private
streams, needs to be optimized. RSMA-OFDMA inherits the same benefits as SDMA-
OFDMA since RSMA builds upon SDMA/MU-MIMO.

####  V-40 Wireless Information and Power Transfer (WIPT)

WIPT has the similarity with ISAC that both systems need to use the spectrum
to deliver two services: either sensing and communications, or power and
communications. Interestingly, similarly to ISAC, the common stream in RSMA
can be helpful to manage multi-user interference and at the same time boost
the performance of the other service, i.e., sensing or power. Consequently,
RSMA has been found more efficient than SDMA and NOMA in WIPT [201, 202].

####  V-41 Vortex Wave Communications/Orbital Angular Momentum

RSMA can be combined with orbital angular momentum (OAM) to benefit from the
flexibility and robustness of RSMA and the additional degrees of freedom of
OAM [203].

####  V-42 Mobile Edge Computing (MEC)

Equipped with powerful computing and storage capabilities, MEC can cope with
the challenges of providing superior and latency-critical computing by
enabling edge users to offload their tasks for nearby processing, therefore
reducing the backhaul bottlenecks, network delays, and transmission costs.
Since RSMA can achieve the full rate boundary of the MAC, while NOMA can
achieve only several separated points on the rate boundary, RSMA can be used
more efficiently than NOMA to aid MEC where multiple users can offload their
tasks while maintaining the QoS of each user [204].

####  V-43 Mixed Criticality

Thanks to the efficiency of RSMA, QoS is enhanced [8, 25]. Thanks to its
flexibility, RSMA can also deliver QoS enhancements in applications where, due
to the diversity of users, services and applications in 6G, mixed criticality
QoS levels are assigned to those users and services [31, 27].

##  VI Myths

Myth 1: RSMA is a special (power-domain) NOMA

It is actually the opposite with (power-domain) NOMA being a special RSMA
technique. In the same way as decoding interference is a particular instance
of RS (and has been known to be so since the 80s and the seminal works on RSMA
[11, 23]), NOMA is a particular instance of RSMA, as illustrated in Fig. 4, 8
and 13. However, one needs to check more carefully at how the schemes have
been built as NOMA is not a special case of all RSMA schemes.

In uplink, NOMA simply relies on SIC. In other words, there is nothing special
to NOMA as it is just an SIC receiver (similarly to the SIC used in spatial
multiplexing for point to point MIMO). RSMA on the hand not only relies on SIC
but also on splitting of the messages at the users. As shown in Section III-B,
by adjusting the split and the power allocation to the resulting streams,
uplink RSMA boils down to uplink NOMA.

In downlink SISO and MISO, all power-domain NOMA schemes are characterized by
having at least one user being forced to fully decode the message(s) of other
co-scheduled user(s) [7]. In the two-user, NOMA (as well as SDMA, OMA, and
physical-layer multicasting) is a subset of RSMA as shown in Section III-A. In
the general $K$-user MISO case as it would depend on the specific RSMA scheme
used. 1-layer RS is a superset of SDMA since by turning off (i.e., allocating
no power to) the common stream, 1-layer RS boils down to MU–LP. On the other
hand, 1-layer RS is not a superset of NOMA. 1-layer RS and NOMA are particular
instances/schemes of the RSMA framework based on the generalized RS relying on
multiple layers of SIC at each receiver, as discussed in Section IV and [7,
25].

In downlink MIMO, there is less research on RSMA. Nevertheless, RSMA is shown
in [7, 35] to outperform and be a superset of NOMA whenever at least one user
is forced to fully decode the multiple streams of other co-scheduled users.
More generally, as it appeared in Table IX and related discussion, RSMA in a
MIMO setting will always be a superset than NOMA because RSMA has the message
splitting capability for each message (and therefore the related interference
management capability), which does not feature in NOMA schemes. This implies
that the optimization space of RSMA will be larger than that of NOMA.

The relationship between SDMA, NOMA, 1-layer RS, 2-layer (hierarchical) RS (as
introduced first in [58] for FDD massive MIMO), and RSMA is further
illustrated in Fig. 31.

Figure 31: Relationship between existing MA strategies and the $K$-user RSMA
framework.

Myth 2: RSMA cannot outperform MU-MIMO

MU-MIMO schemes can rely on linear or nonlinear precoding schemes [6]. For
both types of precoders, we can design linear precoded or non-linear precoded
RSMA that are always a superset of MU-MIMO, and would therefore always achieve
at least the same performance as MU-MIMO.

Taking $K$-user 1-layer RS for simplicity (but the discussion holds for other
RSMA architectures), by decreasing the amount of power allocated to the common
stream, 1-layer RS progressively converges to $K$-user SDMA/MU-MIMO and in the
limit where no power is allocated to the common stream, $K$-user 1-layer RS
swiftly boils down to $K$-user SDMA/MU-MIMO. Hence, 1-layer RS really builds
upon SDMA/MU-MIMO and SDMA/MU-MIMO is a subscheme of 1-layer RS, which
provides a guarantee to 1-layer RS that its rate and DoF are always the same
or better than those of SDMA/MU-MIMO. The same observation holds for non-
linear precoded RSMA as discussed in [109, 108] for Tomlinson-Harashima
precoded RSMA and in [9, 165] for dirty paper coded RSMA. Note that since
dirty paper coding achieves the capacity of MIMO Gaussian BC with perfect
CSIT, applying dirty paper coded RSMA to a perfect CSIT setting would end up
allocating zero power to the common streams, however non-zero power would be
allocated to common streams to boost the performance in imperfect CSIT
settings [9, 165]. Other types of non-linear precoded RSMA schemes are left
for further investigations.

This is completely different from NOMA. NOMA does not build upon SDMA/MU-MIMO.
With $G$ groups, $K$-user MISO NOMA can boil down to $G$-user SDMA by turning
off the power to the weaker users in each group, but $K$-user MISO NOMA can
mathematically never boil down to $K$-user SDMA [7]. The rate/DoF of $K$-user
NOMA can therefore be worse than that of $K$-user SDMA [7].

Myth 3: RSMA is only beneficial for multi-antenna downlink

RSMA is definitely beneficial in multi-antenna downlink, but RSMA is also
beneficial in single-antenna downlink, single/multi-antenna uplink, in multi-
cell, and in relaying.

(a) $R_{k}^{th}=0$ (b) $R_{k}^{th}=0.2$ bit/s/Hz

Figure 32: Sum-rate vs. SNR comparison in three-user SISO BC with perfect
CSIT, $K=3,\sigma_{1}^{2}=1,\sigma_{2}^{2}=0.3,\sigma_{3}^{2}=0.1$.

In single-antenna downlink (SISO BC), the benefits of RSMA depends on whether
the BC is degraded or non-degraded. In the degraded BC, RSMA boils down to
NOMA (since NOMA is capacity achieving for degraded BC) but RSMA can be used
to decrease the receiver complexity and still come close to the capacity
region with a reduced number of SIC layers compared to NOMA [8]. We illustrate
in Fig. 32 a three-user example to compare the sum rate of NOMA and 1-layer RS
when users’ channel variances are
$\sigma_{1}^{2}=1,\sigma_{2}^{2}=0.3,\sigma_{3}^{2}=0.1$ and users are with
and without QoS rate constraints. In the two subfigures, 1-layer RS
respectively achieves 99.84% and 97.65% rate of NOMA while only a single layer
of SIC is required at each user (instead of 2 for NOMA). In the non-degraded
BC, RSMA achieves a strictly larger rate region than NOMA (see Remark 4).
Examples for non-degraded SISO BC comprise (but are not limited to) multi-cell
BC or BC with interference [205], BC with imperfect CSIT [45], RIS-assisted BC
[206], IC with moderate or low interference [11]. Intuitively the underlying
feature of these examples is, that the channels to the receivers cannot be
ordered. For all examples above, there exists reasons, why the channels,
either because there are some carriers where one channel is better than the
other and vice versa, or because on some channel realizations one user
receives more or less interference than the other, or because the channel
state is not perfectly known at the transmitter and therefore, the encoder
cannot determine the optimal pre-coding order. In practice, we do not have
perfect CSIT. Therefore, we will always have to operate on non-degraded SISO
BCs.

In uplink (MAC), RSMA achieves every point at the boundary of the capacity
region without time sharing [23]. As illustrated in Fig. 25, RSMA can also
enhance the spectral efficiency when proportional user fairness is considered
[65].

In multi-cell (IC), RSMA outperforms SC and SIC in the weak interference
regime. It enables an enhanced interference management of the intra-cell and
inter-cell interference as the amount of intra/inter-cell interference to be
canceled at the transmitters or decoded at the receivers can be flexibly
adjusted. RSMA therefore achieves improved spectral and energy efficiency over
the conventional coordinated schemes without RSMA [141, 60].

In relaying and cooperative systems, by enabling the users with strong channel
strength to relay and forward the common stream to the users with weaker
channel strength, not only can cooperative RSMA improves spectral and energy
efficiency [53, 207], but also offering substantial benefits in terms of
coverage extension [25].

Myth 4: The common stream in RSMA is a multicast stream required for multiple
users

A common stream in RSMA is multicasted at the physical layer since it is to be
decoded by multiple users. However the content of the common stream is not
necessarily intended to those users. This is a difference from multicasting
and broadcasting where a message is genuinely intended to multiple users, and
therefore decoded by multiple users. In RSMA, the common stream is created for
interference management purpose, not because the content of the common stream
is intended to multiple users.

It is also possible to do multicasting on top of RSMA. This is the case where
$K$ users want to receive unicast messages $W_{1},\ldots,W_{K}$ (one for each
user), but additionally a multicast message $W_{0}$ is transmitted and
intended to all $K$ users. In that case, RSMA can be used to encode in a
common stream the multicast message $W_{0}$ along with parts of the unicast
messages $W_{1},\ldots,W_{K}$, as discussed in NOUM subsection V-27 [127,
165].

Recall that NOMA also has a common message/stream, though commonly not denoted
using such terminology in the NOMA literature. Hence, the common message is
not a message that is originally intended for all users. It is required to be
decoded by all users but is not necessarily intended for all users.

Myth 5: As the common stream needs to be decoded by multiple users, it causes
privacy/security issues

Note that decoding the common stream at the physical layer does not imply the
sharing of data as encryption is commonly implemented at higher layers and
decryption is performed using user-specific codes. Same would go for other
schemes relying on SIC and interference decoding such as NOMA. There is
therefore no privacy/security issues as long as higher layer encryption is
performed. However, from a physical layer security/secrecy perspective [208,
209], the problem is different and RSMA can be designed to maximize the
secrecy rate as discussed in Subsection V-17.

Myth 6: The message of each user is required to be split into one or multiple
common parts and a private part

In RSMA, the message of each user could be split into one or multiple common
parts and a private part, but is not always required to be split. Whether the
message of a single user or the messages of multiple users are split depends
on the objective function. For instance in Example 1, both messages $W_{1}$
and $W_{2}$ are split, but it could happen that only $W_{1}$ is split or only
$W_{2}$ is split.

Splitting the message of a single user (as in [18]) or more users (as in [8])
at the transmitter has no impact on the performance if the objective is to
maximize the WSR or EE (defined by sum rate dividing the sum transmit power)
subject to transmit power constraint. In such case, the major question is
whether RS is helpful or not, and how much of the total (sum) information
should be carried by the common message regardless of how the common message
is split.

However, if more user fairness is considered, i.e., when the objective is to
maximize the minimum rate among users or/and subject to QoS rate constraints
for each user, the choice of which users to split the messages at the
transmitter will influence the final performance. The best method is to leave
the possibility to split the messages of all users so as to provide rooms for
allocating the rate of the common stream among users.

Myth 7: RSMA has to sacrifice a higher receiver complexity in order to
outperform (power-domain) NOMA

Because RSMA does not enforce a given stream to be fully decoded or to be
fully treated as noise, but rather split one or multiple messages such that a
message is partially decoded by another users, RSMA can explore a wider space
of communication schemes. This consequently leads to relatively simple schemes
like 1-layer RS that relies on a single SIC to outperform multi-SIC NOMA
schemes. It was for instance shown in [7] how the DoF, and therefore rate, of
1-layer RS can be significantly larger than that of complicated NOMA schemes.
Also RSMA builds upon SDMA/MU-MIMO and the addition of SIC layers comes with a
performance enhancement. This contrasts with NOMA where the performance (DoF
and rate) can degrade as we increase the number of SIC, as a consequence of
the restrictive design philosophy of NOMA [7]. The above discussion is further
illustrated in Fig. 27.

Myth 8: With more data streams to send from the transmitter, beamforming
design and power control become very complicated in RSMA

For any MA scheme, the larger the number of users and streams to serve, the
higher the complexity for beamforming and power control. Hence this is not an
issue specific to RSMA, but would hold for SDMA, NOMA, etc. Nevertheless, with
RSMA, low complexity beamforming and power control can be perofrmed. For
instance, in 1-layer RS, private streams could be precoded using zero-forcing
beamforming (ZFBF) and have the power allocated uniformly (that would be much
compliant with the way MU-MIMO is implemented in practice in 4G and 5G). The
precoder of the common streams can be designed using low complexity techniques
[25, 58, 66, 24]. What remains to be designed is the power allocated to the
common stream that could depend on the network load, propagation conditions,
and metric [24, 66, 58].

Myth 9: RSMA requires high SNR to achieve performance gain over NOMA or SDMA

The gain of RSMA over SDMA and NOMA depends on many parameters, including
network load, propagation conditions, objective function and QoS constraints,
and CSIT quality. The more we favour fairness (e.g., MMF objective function,
WSR, QoS constraints), the higher the gain of RSMA over a wide range of SNR.
The lowest gain of RSMA would be experienced in massive MIMO regime with sum-
rate maximization and accurate CSIT. In such scenario, RSMA would boil down to
SDMA or allocate a very small amount of power to the common streams only at
high SNR. We here illustrate a MMF rate comparison result when the network is
overloaded and the CSIT is imperfect, as per Fig. 33. The relative rate gain
of 1-layer RS over MISO NOMA ($G=3$) is 35% when SNR is 15 dB, and 21% when
SNR is 10 dB, which is still non-negligible. Importantly, recall that this
gain is achieved with a single SIC layer, while NOMA ($G=1$) and NOMA ($G=3$)
require 5 and 1 SIC layers, respectively, and achieve much worse MMF rate.

Figure 33: Max-min fairness rate vs. SNR comparison in MISO BC with imperfect
CSIT, $\alpha=0.5$, $M=5$, $K=6$, $\sigma_{k}^{2}=1,\forall k\in\mathcal{K}$.
[7].

Myth 10: RSMA only works when instantaneous channel state information is
available at the transmitter

RSMA can operate on any form of CSIT, such as instantaneous CSIT with high or
low accuracy and various CSI acquisition mechanisms [18, 68, 104], delayed
CSIT [66], or statistical CSIT based on for instance second order statistics
of the channel, e.g., spatial covariance matrix [91, 58, 92]. A detailed
summary of the imperfect CSIT models that have been adopted in the existing
works of RSMA can be found in [25].

Myth 11: The gain of RSMA in massive MIMO is marginal

The gain of RSMA in massive MIMO with perfect CSIT is negligible or inexistent
since the transmitter can form pencil beams that would provide high
beamforming gain and simultaneously eliminate multi-user interference. Hence
private streams are sufficient and RSMA boils down to conventional SDMA-based
massive MIMO.

As we depart from perfect CSIT and considers practical imperfection in CSIT
acquisition, RSMA starts providing gains. In [66], RSMA was shown to provide
significant gains in massive MIMO and to maintain multi-user connectivity in
mobility conditions despite the delayed CSIT. In [91, 58], RSMA was applied to
resolve the imperfect CSIT problem in FDD massive MIMO and was shown to
provide performance gain over conventional massive MIMO based on SDMA
processing. In [68, 104], RSMA was investigated in TDD massive MIMO and cell-
free massive MIMO and was shown to be robust to pilot contamination and to
provide significant gains whenever there is a likelihood of users being
allocated the same uplink pilot sequence. This is motivated by mMTC where the
probability of multiple users sharing the same pilot is very high. In [100],
RSMA was used to enhance the robustness of massive MIMO in the presence of
hardware impairments and in particular phase noise. RSMA was shown to
outperform conventional massive MIMO in the presence of phase noise. In [119],
RSMA was finally shown to be robust in multi-pair massive MIMO relay systems.

##  VII Frequently Asked Questions

We here classify frequently asked questions about RSMA in three different
categories, namely principles and benefits, standardization and
implementation, and applications and interplay with other technologies.

###  VII-A Principles and Benefits of RSMA

Question 1: What is the design principle of RSMA?

In the downlink, the design principle of RSMA is much different to SDMA and
NOMA.

The NOMA design philosophy is based on having a stream to be fully decoded by
another user. For instance, in the MISO case, NOMA forces one user to fully
decode all streams in a group, i.e., its intended stream and the co-scheduled
streams in the group. This leads to the strong constraint that the entire
message of one of the users is mapped onto a common stream, e.g., $W_{2}$
mapped to $s_{\mathrm{c}}$ decoded by both user-1 and user-2 in Table IV.

This is radically different from SDMA design philosophy where messages are
independently encoded into private streams and each receiver decodes its
intended stream treating any residual multi-user interference as noise (even
when the interference level is not weak enough to be treated as noise), as per
Table IV. Interference is never decoded at the receivers due to the absence of
common stream(s).

In RSMA, a message of a given user is not forced to be treated as noise or be
decoded by a user. Instead we have full flexibility on how to encode it and
can evolve in the grey zone in-between. Hence, similarly to SDMA, in MISO
downlink, $K$ private streams are enabled, but in contrast to SDMA, each user
can partially decode the message of another user thanks to the presence of the
common streams. In contrast to NOMA, no user is forced to fully decode the
stream(s) of a co-scheduled user since all private streams are encoded
independently and each receiver decodes its intended private stream treating
any residual interference from the other private streams as noise.

This comes with huge benefits as RSMA builds non-orthogonal transmission
strategies upon SDMA (and therefore MU-MIMO) so that the performance benefits
of SDMA are guaranteed but extra performance is observed by the use of SIC
receivers. Indeed, a performance gain over SDMA is expected from a more
complex receiver architecture. To achieve this, one should enable the
versatility at the transmitter to encode messages such that parts of them can
be decoded by all users using SIC while the remaining parts are decoded by
their intended receivers and treated as noise by non-intended receivers. This
is uniquely achieved with RSMA by providing flexibility in the message-to-
streams mapping, as demonstrated in Tables IV,VII, and IX. Indeed, instead of
keeping a rigid mapping of a message into a predefined stream (as in SDMA and
NOMA), RSMA allows each user to send part of a message in one or multiple
common stream(s) and the rest in one of the $K$ private streams. By adjusting
the power levels of the common and private streams, one can adjust the amount
of interference that occurs on the private stream, so that its level can be
weak enough to be treated as noise. This enables RSMA to manage multi-user
interference by partially decoding the interference and treating the remaining
interference as noise.

In the uplink, the design philosophy of RSMA and NOMA is similar at the
receivers since both aim at using SIC to decode all incoming streams. However,
for a fixed number of users, the number of streams to decode at the receivers
is different because of the specific features in the encoding operation at the
transmitters between RSMA and NOMA. NOMA philosophy is to map a message into a
stream such that in a two-user system, NOMA decodes user-1 before or after
user-2. On the other hand, RSMA splits a message (say of user-1) into sub-
messages, encodes them into independent streams, and superposes them. This
split enables the receiver to decode part of user-1’s message before user-2
and the other part of user-1’s message after user-2.

Question 2: What are the major benefits of RSMA?

Universal: RSMA is a more general multiple access framework that unifies and
generalizes (and consequently outperforms) OMA, SDMA (and multi-antenna) NOMA.
OMA, SDMA, and NOMA, (and other schemes as physical-layer multicasting) are
all particular instances of RSMA [8, 24].

Flexible: RSMA is flexible to cope with all user deployments (with a diversity
of channel directions, channel strengths), network loads (underloaded and
overloaded regimes), and interference levels (weak, medium, strong). RSMA
automatically reduces to other MA techniques according to the channel
conditions, i.e., it reduces to SDMA when user channels are orthogonal in the
underloaded MISO BC with perfect CSIT. When the channels are aligned with
certain channel strength disparities, it reduces to power-domain NOMA. For
other channel conditions, RSMA takes advantage of the common streams and more
efficiently manages multi-user interference by partially decoding the
interference and partially treating the remaining interference as noise [8,
24].

Robust: RSMA is robust to any CSIT inaccuracy [14, 18]. This is very relevant
in modern downlink multi-antenna deployments. While OMA, NOMA, SDMA, all incur
a DoF loss in the presence of imperfect CSIT, RSMA is DoF-optimal and
therefore, less sensitive to CSIT inaccuracy.

Spectrally efficient: The spectral efficiency of RSMA is always larger than or
equal to that of existing MA techniques. Considering a downlink without QoS
constraints, the rate region of RSMA comes much closer to the capacity region
(achieved by DPC) than SDMA and NOMA when CSIT is perfect [8]. When CSIT
becomes imperfect CSIT, linearly precoded RSMA is able to achieve a larger
rate region than complex DPC (and SDMA and NOMA) [9]. As RSMA achieves the
optimal DoF in both perfect and imperfect CSIT [19], it optimally exploits the
spatial dimensions and the availability of CSIT. This contrasts with SDMA and
NOMA that are suboptimal [7]. Considering an uplink, RSMA outperforms NOMA
without time sharing [23, 69].

Energy efficient: Thanks to its flexibility and universality, the EE of RSMA
is also larger than or equal to that of existing MA techniques (OMA, SDMA,
NOMA) in a wide range of user deployments [63, 127, 128].

Enhancing QoS and fairness: RSMA exhibits an even larger performance gain over
other MA techniques whenever each user is subject to a QoS rate constraint or
whenever a higher weight is allocated to the user with a weaker channel
condition [8, 7, 25]. Therefore, the ability of a wireless network
architecture to partially decode interference and partially treat interference
as noise leads to enhanced QoS and user fairness.

Reducing complexity: RSMA has the double benefit of simultaneously boosting
the performance and decreasing the complexity (at the transmitter and the
receiver) compared with multi-antenna NOMA. Recall indeed that multi-antenna
NOMA that requires user grouping, ordering, switching (between NOMA and SDMA)
at the transmit scheduler and multiple layers of SIC at the receivers. On the
other hand, 1-layer RS without any user ordering, grouping or dynamic
switching at the transmit scheduler and with a single layer of SIC at each
receiver is capable of achieving significant performance gain over NOMA [7].
In contrast to SDMA that requires user pairing to pair users with semi-
orthogonal channels, RSMA is suited to all channel conditions and it does not
require complex user scheduling and pairing [58]. Moreover, RSMA is capable of
further reducing CSI feedback overhead in the presence of quantized feedback
[47]. RSMA nevertheless incurs a higher receiver complexity than SDMA due to
the use of SIC.

Reducing latency and improving reliability: Reducing the transmit packet size
is widely known as one major approach of achieving low-latency communications,
also known as, short-packet transmission [210]. RSMA for short packet
transmission has been investigated in [32, 34, 69] for both downlink and
uplink, and RSMA has been shown to use shorter blocklength and therefore lower
latency to achieve equal sum rate and MMF rate with SDMA and NOMA. It
therefore has a great potential to enhance URLLC services in 6G [28].

Question 3: What does the common message contain? From Example 1, we note that
the common message contains information bits from the original unicast (user-
specific) messages of user-1 and user-2. In that example, both messages
$W_{1}$ and $W_{2}$ are split to create the common message; hence the common
message carries some bits from user-1 and from user-2. But we could have
instances where only one of the users message is split, say $W_{1}$ only, and
in such case, the common message carries some bits of user-1 only. The common
message, whether it carries bits of one user or multiple users, is always
decoded by multiple users.

Question 4: Why do we combine the common parts of user messages into a single
common message?

Transmitter and receiver design is greatly simplified by combining the common
parts of multiple users into a single common message, as shown with the two
RSMA strategies of the 2-user downlink in Section III-A1. Moving to a $K$-user
scenario, if each user’s message is split into two parts without combining the
messages, the sender would have to encode the $2K$ message and design $2K$
precoders. Each user needs more SIC layers and the decoding order needs to be
optimized at the transmitter. In contrast, the 1-layer RS transmitter only
encodes and precodes $K+1$ streams, and only one layer of SIC is needed for
each user, hence no worry about decoding order. This greatly reduces the
complexity of 1-layer RS.

Question 5: How does the optimized rate allocation for the common stream guide
the practical message split at the transmitter and the modulation and coding
scheme?

The reader is invited to consult [25] for detailed examples of practical
message splits and modulation and coding schemes.

Question 6: In what scenarios can RSMA achieve explicit performance gain over
SDMA and NOMA?

Let us start with the downlink. From a DoF perspective, RSMA achieves the same
or higher DoF than SDMA and than NOMA with a lower number of SIC layers [7].
This means that in the high SNR regime, RSMA will always outperform those two
schemes and the gains would be larger as the objective functions accounts for
fairness, the system gets more overloaded, or the quality of the CSIT
degrades. A DoF gain also translates to a rate gain at finite SNR though the
exact gain depends on the disparity of channel strengths among users and the
angle between user channels.

In the low to medium SNR regime, the gains of RSMA over SDMA and NOMA will
depend on the user channel orthogonality and the disparity of channel
strength, as shown for a 2-user scenario in Fig. 26. SDMA favors orthogonal
channels, accurate CSIT and similar channel strengths among users. NOMA
favours aligned channels in each group and a large disparity of channel
strengths. As we depart from those extremes, or as the CSIT quality degrades,
RSMA provides explicit gains over SDMA and NOMA even more when constraints on
fairness, QoS, or minimum rate are imposed by the network [24, 8, 25].

RSMA was shown to reduce to SDMA in the presence of orthogonal channels (or
close to orthogonal) and outperform SDMA otherwise. RSMA was shown to reduce
to NOMA and achieve the same rate performance whenever: 1) the SNR is low, 2)
the channels are closely aligned, 3) there is a sufficiently large disparity
of channel gains, and 4) the CSIT is perfect. If all four conditions are met,
NOMA, RSMA, and DPC schemes achieve very similar performance (if not the same
performance). As we depart from those conditions, NOMA incurs a loss over
RSMA. Same observations also hold in more general $K$-user settings [9].

When it comes to the role played by the channel gain disparity among users, it
is important to note that with RSMA, the larger the disparity of channel
gains, the larger the benefits of using RSMA schemes with multiple SIC. In
other words, given a cell size, the disparity of channel strengths among users
could be statistically obtained using the conventional path loss model, and
the designer can then decide how many SIC would be worth given the complexity
that can be afforded. Nevertheless, 1-layer RSMA already brings significant
gain even for realistic channel strength disparities. For instance, in Fig.
27, with 10dB path loss difference and additional Rayleigh fading, 1-layer RS
with a single layer of SIC outperforms NOMA with five SIC layers. Further
gains could be obtained by using RSMA scheme with say two SIC, but this shows
how powerful, RSMA is to efficiently make use of the SIC architecture, and
therefore reduce receiver complexity [7].

In the uplink, NOMA is heavily dependent on time sharing to achieve good
performance and attain capacity. The gain of RSMA over NOMA is explicit
whenever we cannot afford time sharing among users. RSMA achieves the capacity
without the use of time sharing, which finds applications in scenarios where
communication overhead and stringent synchronization requirements due to the
coordination of the transmissions of all users is not possible [23]. This
occurs for instance in services requiring grant-free access, which allow
collisions to reduce the access latency stemming from the channel grant
procedure [25, 88, 192]. Other uplink scenarios where RSMA outperforms NOMA is
in uplink with finite blocklength [69] or in network slicing [193].

The reader is also invited to consult the many applications and scenarios
discussed in Section V where references have demonstrated that RSMA
outperforms SDMA and NOMA.

Question 7: What can we learn from information theory about RSMA? How can
information theory guide the modern study of RSMA?

As re-visited in Section II, the roots of RS can be dated back to [10, 11],
where the coding scheme was introduced for the SISO IC in the weak
interference regime. At that time, the MAC capacity region was characterized
completely by separate random coding and SIC at the receiver. The capacity
region of the degraded BC was also known to be achieved by SC and SIC. Initial
results on the IC showed that capacity region can be achieved by the same
techniques in the case of very strong [40] and strong [211] interference. The
basic idea to develop a coding scheme which allows the receives to decode part
of the interference, to bridge the two extreme cases treat interference as
noise (TIN) and decode interference, led to the development of RS. A very
important ingredient in the development of the best achievable rate region for
the IC is time sharing [212]. Due to the remaining interference, the
achievable rate region is non-convex. Only time sharing between different
coding and decoding strategies including TIN, FDMA, and RS can guarantee the
best achievable rate region. It must be stressed that there is a gap to the
outer bound of the capacity of IC with weak interference. A characterization
of the capacity region within a finite number of bits is derived in [30]. The
achievable scheme in this work is based on a simplified RS approach.

In the BC and IC, the RS approach was introduced in recent works in the
context for multi-antenna settings (MISO and MIMO) with imperfect CSIT .
Though the Gaussian MIMO BC with perfect CSIT is known and achieved by DPC
[42], the capacity and capacity-achieving schemes of those channels with
imperfect CSIT are still unknown, but RS is known to play a crucial role in
achieving optimality in DoF [18, 19, 22, 46, 20, 21], generalized DoF [213,
17, 214], and constant gap [215, 216].

In the MAC, the RS approach was first introduced in [23]. The motivation
behind this development was to apply single-user coding without requiring
synchronization among users. There the term RSMA was coined. Already in [23],
the important cases with fading and interference were considered to bring the
proposed coding and decoding scheme to practical applications.

In multihop-multiflow communication, e.g., in a $2\times 2\times 2$ setting
comprised of 2 sources, 2 relays, and 2 destinations, the combination of RS
with decode-and-forward and amplify-and-forward schemes plays a crucial role
in achieving the fundamental limits [217].

RS is also a crucial ingredient in the theoretical and practical principles of
the broadcast approach to communication over state-dependent channels and
networks [218]. This is relevant in scenarios where the transmitters have
access to only the probabilistic description of the time-varying states while
remaining unaware of their instantaneous realizations [218].

Later the term multiple access was used also for scenarios in which several
users share a link including BC. The term (power-domain) NOMA was introduced
mainly for the downlink transmission corresponding to BC setup. It corresponds
to SC SIC [219] and achieves the capacity region in degraded BC [220]. The
term RSMA was also applied to the BC in [8] where it can provide significant
achievable rate gains.

Network information theory [12] provides the solid basis for system modeling,
a taxonomy of known results with achievable encoding and decoding schemes with
unique taxonomy, and a toolbox of methods and schemes for the modern study of
multi-user communication systems. All currently standardized and developed
transceiver schemes have their roots in the fundamental information theoretic
results - even if the name and the terminology might have changed.

###  VII-B Standardization and Implementation of RSMA

Question 8: What is the status of RSMA standardization? Why would RSMA succeed
in 6G when NOMA was not well received in 5G?

RSMA is still very new in 3GPP and has not been discussed by standard bodies
yet. The machinery required for RSMA is nevertheless already partially being
studied, discussed and developed in 3GPP. Indeed, past 3GPP study/work items
such as MU-MIMO, full-dimensional MIMO, coordinated multi-point (CoMP), multi-
user superposition transmission (MUST), NOMA, network-assisted interference
cancellation and suppression (NAICS), multicast and broadcast services, can be
leveraged to design RSMA. However the key novelty of RSMA, namely relying on
message split, has not been discussed in standardization bodies. Inversely,
RSMA, once introduced in the standard, would address numerous issues and
therefore boost the performance of all those work items.

NOMA was heavily investigated in 5G but not so well received at the end. From
a theoretical point of view, it is predictible that (power-domain) NOMA would
not fly given its deficiencies and the lack of clear gains (and even loss)
over MU-MIMO [7, 221]. In contrast, RSMA does not suffer from those issues as
it really builds upon SDMA/MU-MIMO. Hence 6G could envision a single
transmission mode based on RSMA as a replacement or an enhancement of the MU-
MIMO transmission mode used in 4G and 5G, but also play numerous other roles
in the entire air interface, such as enabling efficient simultaneous unicast-
multicast-broadcast transmissions, with numerous new applications in
automotive driving, VR, 360 video, metaverse, etc.

Question 9: Does RSMA create more complications for implementation?

The 1-layer RS strategy and its benefits in terms of implementation and
complexity over SDMA and NOMA have been already discussed above. There are
nevertheless other challenges to overcome to make RSMA practical. In most of
the RSMA works, its transmitter-side design assumes Gaussian inputs, and it
can be tricky to fit a Gaussian-optimized RSMA-based precoder into a real
physical layer, where it often deals with the finite blocklength and finite
constellations, or with pre-standardized MCS. Interestingly, recent efforts
have been made to make the 1-layer RS strategy work with the state-of-the-art
channel codes and modulation and significant throughput performance have been
observed using realistic link-level simulations [33, 74, 75, 35, 76, 77],
though more work is needed in this area.

Moving to other RSMA schemes, like the generalized RS strategy [8], can be
complex to implement especially for a large number of users. Nevertheless, as
a generalized framework of RSMA, it embraces SDMA, NOMA, physical-layer
multicasting, OMA as special cases, and suggests a novel method to softly
bridge existing MA techniques without using naive hard switching.

Moreover, the generalized RS is applicable to the scenarios with relatively
small $K$ and it achieves non-negligible performance gain over existing MA
techniques. Hence, the transmitter could schedule a small number of users in
each resource block.

Another use of the generalized RS is to act as a benchmark to demonstrate the
performance of other low-complexity RS strategies such as 1-layer RS and
2-layer HRS. It enables to identify which common streams are effective or
ineffective, and consequently trim the generalized RS into a low complexity RS
scheme that would rely on a subset of the common streams. From our experience,
in many applications, low-complexity RS strategies achieve performance
reasonably close to that of generalized RS while their complexities are much
lower than the generalized RS and NOMA strategies. This demonstrates that by
departing from the extremes of treating interference as noise and fully
decoding interference, one can find alternative MA strategies that are
spectrally and energy efficient and simultaneously computationally efficient
(relatively low complexity and small number of SIC layers). It helps us draw
the conclusion that 1-layer RS is a good alternative to the generalized RS in
many practical scenarios.

Another benefit of generalized RS is the ability to come up with a set of
schemes whose performance improve as the number of SIC layers increases. This
is helpful to figure out the performance gap vs complexity tradeoff between
low-complexity RS strategies and generalized RS and understand whether the
addition of one or multiple common streams is worth the complexity increase.
This contrasts with NOMA where a larger number of SIC layers can lead to a
lower performance [7].

Therefore, the generalized RS is a significant strategy in the framework of
RSMA.

###  VII-C Applications and Interplay between RSMA and other Wireless
Technologies

Question 10: Can RSMA be integrated with other MA techniques such as OFDMA,
SDMA, power-domain NOMA, and code-domain NOMA?

RSMA can definitely be combined with OFDMA in the same way as it is done with
SDMA/MU-MIMO in 4G and 5G, namely a group of users are paired and served using
RSMA on a given resource block or subband. Though much remains to be
investigated in OFDMA-RSMA design, some research on the user grouping and
power allocation optimization has been initiated in [172, 171].

Interplay between SDMA or power-domain NOMA and RSMA would not bring benefits,
since SDMA is always part of any RSMA scheme, namely when it comes to the
transmission and reception of the private streams, and power-domain NOMA is
part of the generalized RS architecture of RSMA.

On the other hand, there is no effort so far on the interplay between code-
domain NOMA and RSMA and this is an area of interest to see whether we can
further enhance RSMA performance by bringing the code-domain dimension. Code-
domain NOMA (CD-NOMA) employs carefully designed interleavers and/or code
sequences to multiplex users. The idea was inspired by the traditional CDMA
[222] or interleaver-division multiple access (IDMA) [223]. Some well-known
examples of CD-NOMA include sparse code multiple access (SCMA) [224] and non-
orthogonal coded access (NOCA) [225]. In SCMA, each user is assigned a sparse
codeword according to its message. To exploit the sparsity introduced by the
codeword, the receiver adopts message passing algorithm for multi-user
detection. In contrast, NOCA assigns each user a dense spreading signature to
fully utilize the available time-frequency resources. The receiver exploits
the low cross-correlation properties of spreading sequences for interference
mitigation/suppression. Nevertheless, it is possible to incorporate the design
principle of CD-NOMA into RSMA. Notice that the split common and private
messages in RSMA can be seen as virtual users. Each of the virtual user can be
assigned a dedicated sequence for enabling code-domain multiplexing. Some
interesting research directions on code-domain RSMA can include but not
limited to: design of sparse/dense codewords, detection and decoding
architectures, resource allocation for optimizing achievable rate and energy
efficiency.

It should also be noted that the RSMA framework can be expanded in the time or
frequency domains to get a space-time or space-frequency RSMA framework as in
[47, 22], as discussed in Section III-A7. This is particularly relevant when
the CSIT quality changes across users and time or frequency or when we deal
with asymmetric downlink or multi-cell framework where the receivers have a
different number of receive antennas.

Question 11: Can RSMA be integrated with emerging waveform, e.g., orthogonal
time frequency space (OTFS), orthogonal delay-Doppler division multiplexing
(ODDM)?

OTFS was recently proposed as a new two-dimensional modulation scheme [226]
[227], which multiplexes information symbols in the delay-Doppler (DD) plane
(or domain) rather than the time-frequency (TF) domain as for conventional
multi-carrier modulation or OFDM schemes in the current 4G/5G cellular and
WiFi networks. The DD domain symbol multiplexing enables a direct coupling of
the transmitted symbols with the channel’s delay-Doppler spread function,
which has nice properties, such as quasi-static, compact, and sparse, to be
exploited to achieve a low channel estimation overhead and full channel
diversity with low complexity receivers [228] [229] [230] [231] [232]. OTFS
has also stimulated additional research on delay-Doppler plane modulation with
orthogonal pulses, such as orthogonal delay-Doppler division multiplexing
(ODDM) [233] [234], which can achieve orthogonality with respect to the
channels’ delay and Doppler resolutions that are generally much smaller than
the symbol duration and subcarrier spacing in conventional OFDM.

ODDM, or general delay-Doppler plane multi-carrier modulation, is a promising
waveform for future wireless systems, particularly on doubly-selective
channels. This type of schemes not only provides robust performance in high-
mobility channels, they can also be a viable choice for the future ISAC, due
to that their transmit/receive pulses and the corresponding ambiguity function
satisfy the orthogonality property with respect to the delay-Doppler
resolutions. Similar to OFDMA, ODDM itself can be employed as an orthogonal
multiple access scheme.

With this property, it is natural to have RSMA combined with OTFS/ODDM
waveform in MISO or MIMO systems. The combination of RSMA and OTFS/ODDM can
provide high system design flexibility in terms of its resource allocation and
optimization, in the mean time having a potential to offering high spectral
efficiency, signal localisation, integrated sensing and communication
capabilities.

Question 12: Can RSMA use discrete signaling without SIC?

To analyze the performance of RSMA, such as achievable rate and energy
efficiency, Gaussian signaling is often assumed. As we know, Gaussian
signaling is the optimal signaling in many channels, e.g., point-to-point
Gaussian channels, Gaussian multiple access channels, and Gaussian broadcast
channels. Hence, assuming Gaussian signaling becomes natural in RSMA. Under
this assumption, RSMA can handle interference with different strengths
effectively.

In practical communication systems, discrete signaling, i.e., coded
modulation, is used. In addition, for some applications with stringent
requirements on decoding complexity and latency, e.g., downlink URLLC
services, TIN is more favorable than interference decoding. If Gaussian
signaling is assumed, low-complexity TIN is only optimal when the interference
is very weak, e.g., see Table III. Indeed, despite being the optimal input
distribution for many channels, Gaussian signaling is also the worst noise. On
the other hand, discrete signaling can behave differently from Gaussian
signaling when being treated as noise. When the interference strength is not
very weak, it is possible for discrete signaling with TIN to achieve a
strictly larger achievable rate region than that for Gaussian signaling with
TIN.

Discrete signaling with TIN, i.e., without SIC, has been investigated in
recent works, e.g., [235, 236, 237, 238]. In [235], a lattice partition based
NOMA scheme was proposed for the single antenna $K$-user Gaussian broadcast
channel. It was rigorously proved that the scheme based on discrete signaling
and TIN is capable of achieving the whole capacity region to within a constant
gap independent of the number of users and channel parameters. The same
results hold true for the aforementioned channel model with only statistical
CSI available at the transmitter side [236]. By further exploiting the
algebraic properties of lattices, the full diversity of the broadcast channel
with block fading and close-to-perfect SIC error performance can be attained
for each user with TIN decoding [237]. Finally, in [238], it was shown in the
first time that the capacity region of the Gaussian (asymmetric) interference
channel can be achieved to within a constant gap by discrete signaling and
TIN.

To unleash the full potential of RSMA in practical communication systems, it
is important to exploit the properties of discrete inputs with low-complexity
TIN in RSMA. In light of the above works [235, 236, 237, 238], discrete
signaling with TIN should also benefit RSMA to achieve a better tradeoff
between performance and complexity.

Question 13: How can machine/federated learning help RSMA? What is the role of
machine/federated learning in RSMA design?

Machine learning (ML) and federated learning (FL) can be used on multiple
fronts.

ML can be used at the receiver of RSMA. In [77], a model-based deep learning
(MBDL) method was used to propose new and practical RSMA receiver designs
exploiting the conventional SIC receiver and the robustness and model
agnosticism of deep learning techniques. Thanks to its ability to generate on
demand non-linear symbol detection boundaries in a pure data-driven manner,
the MBDL receiver was shown to significantly outperform conventional SIC
receiver with imperfect CSIR.

ML can be used at the transmitter of RSMA to optimize the resource allocation,
power allocation, task offloading (as in MEC), and beamforming design as an
alternative to conventional convex optimization methods. In [239], the power
allocation for each transmit stream was designed using a deep reinforcement
learning algorithm. It was shown that RSMA achieves a significant performance
gain over SDMA.

Different from the centralized ML method, FL uploads trained model parameters
rather than raw data [240]. However, when training involves wireless edge
devices at the edge network, communications could become a significant
problem. In conventional FL, TDMA is typically used for uplink transmission.
However, the central cloud has to wait until it receives information from all
the user. RSMA enables multiple users to upload information by sharing the
uplink channel at the same time. Thus, by incorporating RSMA in the FL
framework, it is expected that the aggregation latency can be reduced while
maintaining model training quality. For example, multiple IoT devices in fog
radio access networks can cooperate to perform a FL task by repeatedly
uploading locally updated models to a cloud server. To overcome the
performance limitations due to finite capacity front-haul links, a rate-
splitting transmission scheme at IoT devices can be used [241]. With flexible
hybrid edge and cloud decoding strategy achieved by RSMA, we can reduce the
completion time of FL while maintaining a specific target global accuracy.

Not only should the PHY layer benefit from the integration of RSMA and ML, but
the focus should also be on cross-layer design such as aiding network
orchestration, to truly assist wireless communication for intelligent 6G [26].
For instance, ML has been used for UAV deployment in combination with RSMA
transmission [182]. RSMA was shown to require less power compared to other MA
schemes.

Question 14: How would RSMA work in Millimeter-wave and Terahertz networks?

Early research on millimeter-wave communication focused on the strong
directionality benefits that could be attained using beamforming. The general
idea was to communicate with each user using a narrow spatial beam. It was
thought that the small beamwidths would in turn generate minimal interference
between users, which would allow for extremely efficient SDMA designs.

The results of millimeter-wave 5G deployments have begun to change this
thinking. The millimeter-wave benefits to 5G NR has been relatively
disappointing, with few devices being scheduled on the large, under-utilized
frequency bands. It is thought that this problem is a result of the beam
blockage issues, which appear to be a much bigger problem in a large-scale
deployment than was initially thought.

To make millimeter-wave and higher frequencies practical for future broadband
wireless access, it is likely that many of the tight spatial directivity
design philosophies will need to change. Beamwidths may need to increase, and
multi-beam communication may become a necessity. This will likely result in
interference becoming the dominant limiting factor as it is in sub-6 GHz
frequencies.

Even in scenarios where blockage is unlikely, the ever-increasing number of
antennas will likely not lead to beamwidths predicted by theory. Ideally, the
number of feedback bits should scale linearly with the number of users [242,
243, 95, 244]. From a standardization perspective, this is almost impossible
to maintain in the long-term. This will result in a broadening of beams and a
mismatch between the beamformers/precoders used and the CSI. This effect will
cause millimeter-wave and terahertz systems to have interference challenges.

There has already been some initial work on the application of RSMA at
millimeter-wave frequency [91]. This work focused on the CSI feedback mismatch
issue and showed a number of benefits that could simplify implementations in
practice. Recently, RSMA has been shown to expand the coverage of terahertz
systems [107].

##  VIII Conclusions

This paper has provided a tutorial on RSMA. To demonstrate how powerful RSMA
is, the tutorial has departed from the oversimplisitc OMA vs NOMA discussion
held in 5G and has re-centered the discussion and design of multiple access
techniques around the key role of interference management. Building upon first
principles, the tutorial has demonstrated how existing multiple access
techniques are fundamentally limited by their inherent interference management
strategy, namely orthogonalization in OMA, treat interference as noise in
SDMA, and decode interference in NOMA. In contrast RSMA schemes build upon the
RS principle which enables to partially decode interference and partially
treat interference as noise. Consequently RSMA has been shown to provide
unique benefits, including enhanced spectral, energy and computation
efficiency; universality by unifying and generalizing OMA, SDMA, NOMA,
physical-layer multicasting; flexibility by coping with any interference
levels, network loads, services, traffic, user deployments; robustness to
inaccurate channel state information (CSI) and resilience to mixed-critical
quality of service; reliability under short channel codes and low latency.

Future systems will see a growing demand for spectrum intensive applications
and for integrated wireless systems. Moreover they will have to face growing
concerns for congested and contested electromagnetic environments (in both
civilian and defense networks). These will push network designers to adopt
sophisticated interference management, multi-user, and multiple access
techniques. Thanks to its deep root in information theory, numerous benefits
and applications, and its superiority over previous generation multiple access
techniques (OMA, SDMA, NOMA), RSMA will play a growing and underpinning role
in next generation communication systems. It is hoped that the RSMA techniques
presented in this article will help inspiring future research in this exciting
new area and pave the way for designing and implementing RSMA in 6G and
beyond.

## References

  * [1] Y. Saito, A. Benjebbour, Y. Kishiyama, and T. Nakamura, “System-level performance evaluation of downlink non-orthogonal multiple access (NOMA),” in _Proc. IEEE Annu. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC)_ , 2013\.
  * [2] L. Dai, B. Wang, Y. Yuan, S. Han, I. Chih-lin, and Z. Wang, “Non-orthogonal multiple access for 5G: Solutions, challenges, opportunities, and future research trends,” _IEEE Commun. Mag._ , vol. 53, no. 9, pp. 74–81, Sept. 2015.
  * [3] Y. Liu, Z. Qin, M. Elkashlan, Z. Ding, A. Nallanathan, and L. Hanzo, “Nonorthogonal multiple access for 5G and beyond,” _Proc. IEEE_ , vol. 105, no. 12, pp. 2347–2381, Dec. 2017.
  * [4] D. Tse and P. Viswanath, _Fundamentals of Wireless Communication_. Cambridge University Press, 2005.
  * [5] A. Goldsmith, _Wireless Communications_. Cambridge University Press, 2005.
  * [6] B. Clerckx and C. Oestges, _MIMO wireless networks: Channels, techniques and standards for multi-antenna, multi-user and multi-cell systems_. Academic Press, 2013.
  * [7] B. Clerckx, Y. Mao, R. Schober, E. A. Jorswieck, D. J. Love, J. Yuan, L. Hanzo, G. Y. Li, E. G. Larsson, and G. Caire, “Is NOMA efficient in multi-antenna networks? A critical look at next generation multiple access techniques,” _IEEE Open J. Commun. Soc._ , vol. 2, pp. 1310–1343, June 2021.
  * [8] Y. Mao, B. Clerckx, and V. O. K. Li, “Rate-splitting multiple access for downlink communication systems: Bridging, generalizing, and outperforming SDMA and NOMA,” _EURASIP J. Wireless Commun. Netw._ , vol. 2018, no. 1, p. 133, May 2018.
  * [9] Y. Mao and B. Clerckx, “Beyond dirty paper coding for multi-antenna broadcast channel with partial CSIT: A rate-splitting approach,” _IEEE Trans. Commun._ , vol. 68, no. 11, pp. 6775–6791, Nov. 2020.
  * [10] A. Carleial, “Interference channels,” _IEEE Trans. Inf. Theory_ , vol. 24, no. 1, pp. 60–70, Jan. 1978.
  * [11] T. Han and K. Kobayashi, “A new achievable rate region for the interference channel,” _IEEE Trans. Inf. Theory_ , vol. 27, no. 1, pp. 49–60, Jan. 1981\.
  * [12] A. El Gamal and Y.-H. Kim, _Network information theory_. Cambridge university press, 2011.
  * [13] A. G. Davoodi and S. A. Jafar, “Aligned image sets under channel uncertainty: Settling conjectures on the collapse of degrees of freedom under finite precision CSIT,” _IEEE Trans. Inf. Theory_ , vol. 62, no. 10, pp. 5603–5618, Oct. 2016.
  * [14] B. Clerckx, H. Joudeh, C. Hao, M. Dai, and B. Rassouli, “Rate splitting for MIMO wireless networks: A promising PHY-layer strategy for LTE evolution,” _IEEE Commun. Mag._ , vol. 54, no. 5, pp. 98–105, May 2016\.
  * [15] M. B. Shenouda and T. N. Davidson, “On the design of linear transceivers for multiuser systems with channel uncertainty,” _IEEE J. Sel. Areas Commun._ , vol. 26, no. 6, pp. 1015–1024, 2008.
  * [16] N. Vucic, H. Boche, and S. Shi, “Robust transceiver optimization in downlink multiuser mimo systems with channel uncertainty,” in _Proc. IEEE Int. Conf. Commun. (ICC)_ , 2008, pp. 3516–3520.
  * [17] A. Gholami Davoodi, B. Yuan, and S. A. Jafar, “GDoF region of the MISO BC: Bridging the gap between finite precision and perfect CSIT,” _IEEE Trans. Inf. Theory_ , vol. 64, no. 11, pp. 7208–7217, 2018.
  * [18] H. Joudeh and B. Clerckx, “Sum-rate maximization for linearly precoded downlink multiuser MISO systems with partial CSIT: A rate-splitting approach,” _IEEE Trans. Commun._ , vol. 64, no. 11, pp. 4847–4861, Nov. 2016.
  * [19] E. Piovano and B. Clerckx, “Optimal DoF region of the $K$-user MISO BC with partial CSIT,” _IEEE Commun. Lett._ , vol. 21, no. 11, pp. 2368–2371, Nov. 2017.
  * [20] H. Joudeh and B. Clerckx, “DoF region of the MISO BC with partial CSIT: Proof by inductive Fourier-Motzkin elimination,” in _Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC)_ , 2019.
  * [21] A. Gholami Davoodi and S. Jafar, “Degrees of freedom region of the ($m$, $n_{1}$, $n_{2}$) MIMO broadcast channel with partial CSIT: An application of sum-set inequalities based on aligned image sets,” _IEEE Trans. Inf. Theory_ , vol. 66, no. 10, pp. 6256–6279, Oct. 2020.
  * [22] C. Hao, B. Rassouli, and B. Clerckx, “Achievable DoF regions of MIMO networks with imperfect CSIT,” _IEEE Trans. Inf. Theory_ , vol. 63, no. 10, pp. 6587–6606, Oct. 2017.
  * [23] B. Rimoldi and R. Urbanke, “A rate-splitting approach to the Gaussian multiple-access channel,” _IEEE Trans. Inf. Theory_ , vol. 42, no. 2, pp. 364–375, Mar. 1996.
  * [24] B. Clerckx, Y. Mao, R. Schober, and H. V. Poor, “Rate-splitting unifying SDMA, OMA, NOMA, and multicasting in MISO broadcast channel: A simple two-user rate analysis,” _IEEE Wireless Commun. Lett._ , vol. 9, no. 3, pp. 349–353, Mar. 2020.
  * [25] Y. Mao, O. Dizdar, B. Clerckx, R. Schober, P. Popovski, and H. V. Poor, “Rate-splitting multiple access: Fundamentals, survey, and future research trends,” _IEEE Commun. Surv. Tutor._ , vol. 24, no. 4, pp. 2073–2126, 2022\.
  * [26] A. Mishra, Y. Mao, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access for 6G—part I: Principles, applications and future works,” _IEEE Commun. Lett._ , vol. 26, no. 10, pp. 2232–2236, Oct. 2022.
  * [27] R.-J. Reifert, S. Roth, A. A. Ahmad, and A. Sezgin, “Comeback kid: Resilience for mixed-critical wireless network resource management,” _arXiv preprint arXiv:2204.11878_ , 2022.
  * [28] O. Dizdar, Y. Mao, W. Han, and B. Clerckx, “Rate-splitting multiple access: A new frontier for the PHY layer of 6G,” in _Proc. IEEE 92nd Veh. Technol. Conf. (VTC Fall)_ , 2020.
  * [29] O. Dizdar, Y. Mao, Y. Xu, P. Zhu, and B. Clerckx, “Rate-splitting multiple access for enhanced URLLC and eMBB in 6G,” in _Proc. IEEE Int. Symp. Wireless Commun. Syst. (ISWCS)_ , 2021.
  * [30] R. H. Etkin, D. N. C. Tse, and H. Wang, “Gaussian interference channel capacity to within one bit,” _IEEE Trans. Inf. Theory_ , vol. 54, no. 12, pp. 5534–5562, Dec. 2008.
  * [31] R.-J. Reifert, S. Roth, A. A. Ahmad, and A. Sezgin, “Energy efficiency in rate-splitting multiple access with mixed criticality,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2022, pp. 681–686.
  * [32] Y. Xu, Y. Mao, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access with finite blocklength for short-packet and low-latency downlink communications,” _IEEE Trans. Veh. Technol._ , vol. 71, no. 11, pp. 12 333–12 337, 2022.
  * [33] O. Dizdar, Y. Mao, W. Han, and B. Clerckx, “Rate-splitting multiple access for downlink multi-antenna communications: Physical layer design and link-level simulations,” in _Proc. IEEE Annu. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC)_ , 2020.
  * [34] Y. Xu, Y. Mao, O. Dizdar, and B. Clerckx, “Max-min fairness of rate-splitting multiple access with finite blocklength communications,” _IEEE Trans. Veh. Technol._ , pp. 1–6, 2022.
  * [35] A. Mishra, Y. Mao, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access for downlink multiuser MIMO: Precoder optimization and PHY-layer design,” _IEEE Trans. Commun._ , vol. 70, no. 2, pp. 874–890, 2022.
  * [36] A. Krishnamoorthy, Z. Ding, and R. Schober, “Precoder design and statistical power allocation for MIMO-NOMA via user-assisted simultaneous diagonalization,” _IEEE Trans. Commun._ , vol. 69, no. 2, pp. 929–945, Feb. 2021.
  * [37] A. Krishnamoorthy and R. Schober, “Uplink and downlink MIMO-NOMA with simultaneous triangularization,” _IEEE Trans. Wireless Commun._ , vol. 20, no. 6, pp. 3381–3396, June 2021.
  * [38] L. Yin, Y. Mao, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access for 6G—part II: Interplay with integrated sensing and communications,” _IEEE Commun. Lett._ , vol. 26, no. 10, pp. 2237–2241, Oct. 2022.
  * [39] H. Li, Y. Mao, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access for 6G—part III: Interplay with reconfigurable intelligent surfaces,” _IEEE Commun. Lett._ , vol. 26, no. 10, pp. 2242–2246, Oct. 2022.
  * [40] A. Carleial, “A case where interference does not reduce capacity (corresp.),” _IEEE Trans. Inf. Theory_ , vol. 21, no. 5, pp. 569–570, 1975.
  * [41] M. Costa, “Writing on dirty paper (corresp.),” _IEEE Trans. Inf. Theory_ , vol. 29, no. 3, pp. 439–441, May 1983.
  * [42] H. Weingarten, Y. Steinberg, and S. S. Shamai, “The capacity region of the Gaussian multiple-input multiple-output broadcast channel,” _IEEE Trans. Inf. Theory_ , vol. 52, no. 9, pp. 3936–3964, Sept. 2006.
  * [43] G. Caire and S. Shamai, “On the achievable throughput of a multiantenna Gaussian broadcast channel,” _IEEE Trans. Inf. Theory_ , vol. 49, no. 7, pp. 1691–1706, July 2003.
  * [44] D. N. C. Tse and R. D. Yates, “Fading broadcast channels with state information at the receivers,” _IEEE Trans. Inf. Theory_ , vol. 58, no. 6, pp. 3453–3471, June 2012.
  * [45] P.-H. Lin, E. A. Jorswieck, R. F. Schaefer, M. Mittelbach, and C. R. Janda, “New capacity results for fading Gaussian multiuser channels with statistical CSIT,” _IEEE Trans. Commun._ , vol. 68, no. 11, pp. 6761–6774, Nov. 2020.
  * [46] S. Yang, M. Kobayashi, D. Gesbert, and X. Yi, “Degrees of freedom of time correlated MISO broadcast channel with delayed CSIT,” _IEEE Trans. Inf. Theory_ , vol. 59, no. 1, pp. 315–328, Jan. 2013.
  * [47] C. Hao, Y. Wu, and B. Clerckx, “Rate analysis of two-receiver MISO broadcast channel with finite rate feedback: A rate-splitting approach,” _IEEE Trans. Commun._ , vol. 63, no. 9, pp. 3232–3246, Sept. 2015.
  * [48] H. Joudeh and B. Clerckx, “Robust transmission in downlink multiuser MISO systems: A rate-splitting approach,” _IEEE Trans. Signal Process._ , vol. 64, no. 23, pp. 6227–6242, Dec. 2016.
  * [49] Y. Liang and V. V. Veeravalli, “Cooperative relay broadcast channels,” _IEEE Trans. Inf. Theory_ , vol. 53, no. 3, pp. 900–928, Mar. 2007.
  * [50] Y. Liang and G. Kramer, “Rate regions for relay broadcast channels,” _IEEE Trans. Inf. Theory_ , vol. 53, no. 10, pp. 3517–3535, Oct. 2007.
  * [51] R. Dabora and S. D. Servetto, “Broadcast channels with cooperating decoders,” _IEEE Trans. Inf. Theory_ , vol. 52, no. 12, pp. 5438–5454, Dec. 2006.
  * [52] J. Zhang, B. Clerckx, J. Ge, and Y. Mao, “Cooperative rate splitting for MISO broadcast channel with user relaying, and performance benefits over cooperative NOMA,” _IEEE Signal Process. Lett._ , vol. 26, no. 11, pp. 1678–1682, Nov. 2019.
  * [53] Y. Mao, B. Clerckx, J. Zhang, V. O. K. Li, and M. A. Arafah, “Max-min fairness of K-user cooperative rate-splitting in MISO broadcast channel with user relaying,” _IEEE Trans. Wireless Commun._ , vol. 19, no. 10, pp. 6362–6376, Oct. 2020.
  * [54] R. Tandon, S. A. Jafar, S. Shamai, and H. V. Poor, “On the synergistic benefits of alternating CSIT for the MISO broadcast channel,” _IEEE Trans. Inf. Theory_ , vol. 59, no. 7, pp. 4106–4128, 2013.
  * [55] J. Chen and P. Elia, “Optimal DoF region of the two-user MISO-BC with general alternating CSIT,” in _Proc. 47th Asilomar Conf. Signals, Syst. Comput._ , 2013, pp. 1860–1864.
  * [56] H. Joudeh and B. Clerckx, “On the separability of parallel MISO broadcast channels under partial CSIT: A degrees of freedom region perspective,” _IEEE Trans. Inf. Theory_ , vol. 66, no. 7, pp. 4513–4529, July 2020.
  * [57] C. Mosquera, N. Noels, T. Ramírez, M. Caus, and A. Pastore, “Space-time rate splitting for the MISO BC with magnitude CSIT,” _IEEE Trans. Commun._ , vol. 69, no. 7, pp. 4417–4432, July 2021.
  * [58] M. Dai, B. Clerckx, D. Gesbert, and G. Caire, “A rate splitting strategy for massive MIMO with imperfect CSIT,” _IEEE Trans. Wireless Commun._ , vol. 15, no. 7, pp. 4611–4624, July 2016.
  * [59] C. Hao and B. Clerckx, “MISO networks with imperfect CSIT: A topological rate-splitting approach,” _IEEE Trans. Commun._ , vol. 65, no. 5, pp. 2164–2179, May 2017.
  * [60] N. Ha, W. Shin, M. Vaezi, and H. V. Poor, “Coordinated rate splitting multiple access for multi-cell downlink networks,” in _Proc. 54th Asilomar Conf. Signals, Syst. Comput._ , 2020.
  * [61] Y. Mao, B. Clerckx, and V. O. K. Li, “Rate-splitting multiple access for coordinated multi-point joint transmission,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2019.
  * [62] A. A. Ahmad, Y. Mao, A. Sezgin, and B. Clerckx, “Rate splitting multiple access in C-RAN: A scalable and robust design,” _IEEE Trans. Commun._ , pp. 1–1, Sept. 2021.
  * [63] Y. Mao, B. Clerckx, and V. O. K. Li, “Energy efficiency of rate-splitting multiple access, and performance benefits over SDMA and NOMA,” in _Proc. IEEE Int. Symp. Wireless Commun. Syst. (ISWCS)_ , 2018.
  * [64] B. Matthiesen, Y. Mao, P. Popovski, and B. Clerckx, “Globally optimal beamforming for rate splitting multiple access,” in _Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP)_ , 2021.
  * [65] Z. Yang, M. Chen, W. Saad, W. Xu, and M. Shikh-Bahaei, “Sum-rate maximization of uplink rate splitting multiple access (RSMA) communication,” _IEEE Trans. Mob. Comput._ , vol. 21, no. 7, pp. 2596–2609, July 2022.
  * [66] O. Dizdar, Y. Mao, and B. Clerckx, “Rate-splitting multiple access to mitigate the curse of mobility in (massive) MIMO networks,” _IEEE Trans. Commun._ , vol. 69, no. 10, pp. 6765–6780, Oct. 2021.
  * [67] H. Joudeh and B. Clerckx, “Rate-splitting for max-min fair multigroup multicast beamforming in overloaded systems,” _IEEE Trans. Wireless Commun._ , vol. 16, no. 11, pp. 7276–7289, Nov. 2017.
  * [68] A. Mishra, Y. Mao, C. K. Thomas, L. Sanguinetti, and B. Clerckx, “Mitigating intra-cell pilot contamination in massive MIMO: A rate splitting approach,” _IEEE Trans. Wireless Commun._ , 2022.
  * [69] J. Xu, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access for short-packet uplink communications: A finite blocklength analysis,” _IEEE Commun. Lett._ , pp. 1–1, 2022.
  * [70] Q. Zhu, Z. Qian, B. Clerckx, and X. Wang, “Rate-splitting multiple access in multi-cell dense networks: A stochastic geometry approach,” _arXiv preprint arXiv:2207.11430_ , 2022.
  * [71] E. Demarchou, C. Psomas, and I. Krikidis, “On the sum rate of miso rate splitting with spatial randomness,” in _Proc. IEEE Int. Symp. Wireless Commun. Syst. (ISWCS)_ , 2021.
  * [72] ——, “Channel statistics-based rate splitting with spatial randomness,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2020.
  * [73] S. A. Tegos, P. D. Diamantoulakis, and G. K. Karagiannidis, “On the performance of uplink rate-splitting multiple access,” _IEEE Commun. Lett._ , vol. 26, no. 3, pp. 523–527, Mar. 2022.
  * [74] H. Chen, D. Mi, Z. Chu, P. Xiao, Y. Xu, and D. He, “Link-level performance of rate-splitting based downlink multiuser MISO systems,” in _Proc. IEEE Annu. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC)_ , 2020.
  * [75] L. Yin, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access for multigroup multicast cellular and satellite communications: PHY layer design and link-level simulations,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2021.
  * [76] R. Cerna-Loli, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access for multi-antenna joint radar and communications with partial CSIT: Precoder optimization and link-level simulations,” _arXiv preprint arXiv:2201.10621_ , 2021.
  * [77] R. C. Loli, O. Dizdar, B. Clerckx, and C. Ling, “Model-based deep learning receiver design for rate-splitting multiple access,” _arXiv preprint arXiv:2205.00849_ , 2022.
  * [78] M. Medra and T. N. Davidson, “Robust downlink transmission: An offset-based single-rate-splitting approach,” in _Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC)_ , 2018.
  * [79] B. Matthiesen, Y. Mao, A. Dekorsy, P. Popovski, and B. Clerckx, “Globally optimal spectrum- and energy-efficient beamforming for rate splitting multiple access,” _IEEE Trans. Signal Process._ , vol. 70, pp. 5025–5040, Oct. 2022.
  * [80] Z. Li, C. Ye, Y. Cui, S. Yang, and S. Shamai, “Rate splitting for multi-antenna downlink: Precoder design and practical implementation,” _IEEE J. Sel. Areas Commun._ , vol. 38, no. 8, pp. 1910–1924, Aug. 2020.
  * [81] J. Park, J. Choi, N. Lee, W. Shin, and H. V. Poor, “Sum spectral efficiency optimization for rate splitting in downlink MU-MISO: A generalized power iteration approach,” in _Proc. IEEE Wireless Commun. Netw. Conf. (WCNC)_ , 2021.
  * [82] A. R. Flores, R. C. De Lamare, and B. Clerckx, “Linear precoding and stream combining for rate splitting in multiuser MIMO systems,” _IEEE Commun. Lett._ , vol. 24, no. 4, pp. 890–894, Apr. 2020.
  * [83] Z. Li, S. Yang, and S. Shamai, “On linearly precoded rate splitting for Gaussian MIMO broadcast channels,” _IEEE Trans. Inf. Theory_ , vol. 67, no. 7, pp. 4693–4709, July 2021.
  * [84] A. Krishnamoorthy and R. Schober, “Successive null-space precoder design for downlink MU-MIMO with rate splitting and single-stage SIC,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2021.
  * [85] H. D. Tuan, A. A. Nasir, M.-N. Nguyen, and M. Masood, “Han–kobayashi signaling in MIMO broadcasting,” _IEEE Commun. Lett._ , vol. 23, no. 5, pp. 855–858, May 2019.
  * [86] C. Kaulich, M. Joham, and W. Utschick, “Efficient rate splitting method using successive beamforming techniques,” in _Proc. IEEE Int. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC)_ , 2020, pp. 1–6.
  * [87] A. Grant, B. Rimoldi, R. Urbanke, and P. Whiting, “Rate-splitting multiple access for discrete memoryless channels,” _IEEE Trans. Inf. Theory_ , vol. 47, no. 3, pp. 873–890, Mar. 2001.
  * [88] H. Liu, T. A. Tsiftsis, K. J. Kim, K. S. Kwak, and H. V. Poor, “Rate splitting for uplink NOMA with enhanced fairness and outage performance,” _IEEE Trans. Wireless Commun._ , vol. 19, no. 7, pp. 4657–4670, July 2020.
  * [89] J. Cao and E. M. Yeh, “Asymptotically optimal multiple-access communication via distributed rate splitting,” _IEEE Trans. Inf. Theory_ , vol. 53, no. 1, pp. 304–319, Jan. 2007.
  * [90] Y. Mao and B. Clerckx, _Multiple Access Techniques_. Cham: Springer International Publishing, 2021, pp. 63–100.
  * [91] M. Dai and B. Clerckx, “Multiuser millimeter wave beamforming strategies with quantized and statistical CSIT,” _IEEE Trans. Wireless Commun._ , vol. 16, no. 11, pp. 7025–7038, Nov. 2017.
  * [92] L. Yin, B. Clerckx, and Y. Mao, “Rate-splitting multiple access for multi-antenna broadcast channels with statistical CSIT,” in _Proc. IEEE Wireless Commun. Netw. Conf. (WCNC)_ , 2021.
  * [93] D. B. Amor, M. Joham, and W. Utschick, “Bilinear precoding for fdd massive mimo system with imperfect covariance matrices,” in _International ITG Workshop on Smart Antennas_ , 2020, pp. 1–6.
  * [94] G. Lu, L. Li, H. Tian, and F. Qian, “MMSE-based precoding for rate splitting systems with finite feedback,” _IEEE Commun. Lett._ , vol. 22, no. 3, pp. 642–645, Mar. 2018.
  * [95] N. Jindal, “MIMO broadcast channels with finite-rate feedback,” _IEEE Trans. Inf. Theory_ , vol. 52, no. 11, pp. 5045–5060, Nov. 2006.
  * [96] J. An, O. Dizdar, B. Clerckx, and W. Shin, “Rate-splitting multiple access for multi-antenna broadcast channel with imperfect CSIT and CSIR,” in _Proc. IEEE Annu. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC)_ , 2020\.
  * [97] A. Adhikary, J. Nam, J.-Y. Ahn, and G. Caire, “Joint spatial division and multiplexing—the large-scale array regime,” _IEEE Trans. Inf. Theory_ , vol. 59, no. 10, pp. 6441–6463, Oct. 2013.
  * [98] J. Chen and V. K. N. Lau, “Two-tier precoding for FDD multi-cell massive MIMO time-varying interference networks,” _IEEE J. Sel. Areas Commun._ , vol. 32, no. 6, pp. 1230–1238, 2014.
  * [99] C. K. Thomas, B. Clerckx, L. Sanguinetti, and D. Slock, “A rate splitting strategy for mitigating intra-cell pilot contamination in massive MIMO,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2020.
  * [100] A. Papazafeiropoulos, B. Clerckx, and T. Ratnarajah, “Rate-splitting to mitigate residual transceiver hardware impairments in massive MIMO systems,” _IEEE Trans. Veh. Technol._ , vol. 66, no. 9, pp. 8196–8211, Sept. 2017.
  * [101] O. Dizdar, A. Kaushik, B. Clerckx, and C. Masouros, “Rate-splitting multiple access for joint radar-communications with low-resolution DACs,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2021.
  * [102] ——, “Energy efficient dual-functional radar-communication: Rate-splitting multiple access, low-resolution DACs, and RF chain selection,” _IEEE Open J. Commun. Soc._ , vol. 3, pp. 986–1006, 2022.
  * [103] S. Park, J. Choi, J. Park, W. Shin, and B. Clerckx, “Rate-splitting multiple access for quantized multiuser MIMO communications,” _arXiv preprint arXiv:2208.00643_ , 2022.
  * [104] A. Mishra, Y. Mao, L. Sanguinetti, and B. Clerckx, “Rate-splitting assisted massive machine-type communications in cell-free massive mimo,” _IEEE Commun. Lett._ , vol. 26, no. 6, pp. 1358–1362, June 2022.
  * [105] O. Kolawole, A. Panazafeironoulos, and T. Ratnarajah, “A rate-splitting strategy for multi-user millimeter-wave systems with imperfect CSI,” in _Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC)_ , 2018.
  * [106] Z. Li, S. Yang, and T. Clessienne, “A general rate splitting scheme for hybrid precoding in mmwave systems,” in _Proc. IEEE Int. Conf. Commun. (ICC)_ , 2019.
  * [107] H. Cho, B. Ko, B. Clerckx, and J. Choi, “Coverage increase at thz frequencies: A cooperative rate-splitting approach,” _arXiv preprint arXiv:2208.04541_ , 2022.
  * [108] A. R. Flores, B. Clerckx, and R. C. de Lamare, “Tomlinson-harashima precoded rate-splitting for multiuser multiple-antenna systems,” in _Proc. IEEE Int. Symp. Wireless Commun. Syst. (ISWCS)_ , 2018.
  * [109] A. R. Flores, R. C. De Lamare, and B. Clerckx, “Tomlinson-Harashima precoded rate-splitting with stream combiners for MU-MIMO systems,” _IEEE Trans. Commun._ , vol. 69, no. 6, pp. 3833–3845, June 2021.
  * [110] A. R. Flores, R. C. de Lamare, and B. Clerckx, “Multi-branch tomlinson-harashima precoding for rate splitting based systems with multiple antennas,” in _Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP)_ , 2021, pp. 4885–4889.
  * [111] J. Zhang and J. Ge, “Cooperative rate-splitting for downlink multiuser MISO systems with partial CSIT,” _IEEE Trans. Commun._ , vol. 69, no. 11, pp. 7504–7519, 2021.
  * [112] B. Matthiesen and E. A. Jorswieck, “Optimal resource allocation for non-regenerative multiway relaying with rate splitting,” in _Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC)_ , 2018.
  * [113] A. Salern and L. Musavian, “Rate splitting in multi-pair energy harvesting relaying systems,” in _Proc. IEEE Int. Symp. Wireless Commun. Syst. (ISWCS)_ , 2018.
  * [114] B. Zheng, X. Wang, M. Wen, and F. Chen, “NOMA-based multi-pair two-way relay networks with rate splitting and group decoding,” _IEEE J. Sel. Areas Commun._ , vol. 35, no. 10, pp. 2328–2341, Oct. 2017.
  * [115] N. H. Khattab and S. S. Soliman, “On data rate maximization using hybrid rate-splitting/noma network diamond relaying,” in _International Conference on Factory Communication Systems (WFCS)_ , 2022, pp. 1–4.
  * [116] T. Cover and A. Gamal, “Capacity theorems for the relay channel,” _IEEE Trans. Inf. Theory_ , vol. 25, no. 5, pp. 572–584, 1979.
  * [117] A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity. part I. system description,” _IEEE Trans. Commun._ , vol. 51, no. 11, pp. 1927–1938, 2003.
  * [118] J. Laneman, D. Tse, and G. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” _IEEE Trans. Inf. Theory_ , vol. 50, no. 12, pp. 3062–3080, 2004.
  * [119] A. Papazafeiropoulos and T. Ratnarajah, “Rate-splitting robustness in multi-pair massive MIMO relay systems,” _IEEE Trans. Wireless Commun._ , vol. 17, no. 8, pp. 5623–5636, Aug. 2018.
  * [120] R. Bassily, E. Ekrem, X. He, E. Tekin, J. Xie, M. R. Bloch, S. Ulukus, and A. Yener, “Cooperative security at the physical layer: A summary of recent advances,” _IEEE Signal Process. Mag._ , vol. 30, no. 5, pp. 16–28, 2013\.
  * [121] H. Fu, S. Feng, W. Tang, and D. W. K. Ng, “Robust secure beamforming design for two-user downlink MISO rate-splitting systems,” _IEEE Trans. Wireless Commun._ , vol. 19, no. 12, pp. 8351–8365, Dec. 2020.
  * [122] P. Li, M. Chen, Y. Mao, Z. Yang, B. Clerckx, and M. Shikh-Bahaei, “Cooperative rate-splitting for secrecy sum-rate enhancement in multi-antenna broadcast channels,” in _Proc. IEEE Annu. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC)_ , 2020.
  * [123] H. Xia, Y. Mao, X. Zhou, B. Clerckx, and C. Li, “Secure beamforming design for rate-splitting multiple access in multi-antenna broadcast channel with confidential messages,” _arXiv preprint arXiv:2202.07328_ , 2022.
  * [124] H. Xia, Y. Mao, B. Clerckx, X. Zhou, S. Han, and C. Li, “Weighted sum-rate maximization for rate-splitting multiple access based secure communication,” _Proc. IEEE Wireless Commun. Netw. Conf. (WCNC)_ , 2022.
  * [125] A. Salem, C. Masouros, and B. Clerckx, “Secure rate-splitting multiple access: How much of the split signal to reveal?” _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2022.
  * [126] M. Abolpour, S. Aïssa, L. Musavian, and A. Bhowal, “Rate splitting in the presence of untrusted users: Outage and secrecy outage performances,” _IEEE Open J. Commun. Soc._ , vol. 3, pp. 921–935, 2022.
  * [127] Y. Mao, B. Clerckx, and V. O. K. Li, “Rate-splitting for multi-antenna non-orthogonal unicast and multicast transmission: Spectral and energy efficiency analysis,” _IEEE Trans. Commun._ , vol. 67, no. 12, pp. 8754–8770, Dec. 2019.
  * [128] G. Zhou, Y. Mao, and B. Clerckx, “Rate-splitting multiple access for multi-antenna downlink communication systems: Spectral and energy efficiency tradeoff,” _IEEE Trans. Wireless Commun._ , vol. 21, no. 7, pp. 4816–4828, 2022.
  * [129] K. Weinberger, A. A. Ahmad, and A. Sezgin, “On synergistic benefits of rate splitting in IRS-assisted cloud radio access networks,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2021.
  * [130] Z. Yang, J. Shi, Z. Li, M. Chen, W. Xu, and M. Shikh-Bahaei, “Energy efficient rate splitting multiple access (RSMA) with reconfigurable intelligent surface,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2020.
  * [131] A. Bansal, K. Singh, and C.-P. Li, “Analysis of hierarchical rate splitting for intelligent reflecting surfaces-aided downlink multiuser MISO communications,” _IEEE Open J. Commun. Soc_ , vol. 2, pp. 785–798, Apr. 2021\.
  * [132] A. Jolly, S. Biswas, and K. Singh, “An analysis on rate-splitting multiple access for IRS aided 6G communication,” _arXiv preprint arXiv:2106.04418_ , 2021.
  * [133] A. Bansal, K. Singh, B. Clerckx, C.-P. Li, and M.-S. Alouini, “Rate-splitting multiple access for intelligent reflecting surface aided multi-user communications,” _IEEE Trans. Veh. Technol._ , vol. 70, no. 9, pp. 9217–9229, Sept. 2021.
  * [134] K. Weinberger, A. A. Ahmad, A. Sezgin, and A. Zappone, “Synergistic benefits in IRS- and RS-enabled C-RAN with energy-efficient clustering,” _IEEE Trans. Wireless Commun._ , vol. 21, no. 10, pp. 8459–8475, 2022.
  * [135] H. Fu, S. Feng, and D. W. K. Ng, “Resource allocation design for IRS-aided downlink MU-MISO RSMA systems,” _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2021.
  * [136] T. Fang, Y. Mao, S. Shen, Z. Zhu, and B. Clerckx, “Fully connected reconfigurable intelligent surface aided rate-splitting multiple access for multi-user multi-antenna transmission,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2022, pp. 675–680.
  * [137] A. S. de Sena, P. H. J. Nardelli, D. B. da Costa, P. Popovski, and C. B. Papadias, “Rate-splitting multiple access and its interplay with intelligent reflecting surfaces,” _IEEE Commun. Mag._ , pp. 1–12, 2022.
  * [138] K. Zhao, Y. Mao, Z. Yang, L. Lian, and B. Clerckx, “Reconfigurable intelligent surfaces empowered cooperative rate splitting with user relaying,” in _Proc. IEEE Int. Symp. Wireless Commun. Syst. (ISWCS)_ , 2022, pp. 1–6.
  * [139] G. Zhou, W. Xu, and G. Bauch, “Interference mitigation with rate splitting in multi-cell wireless networks,” in _Proc. IEEE Int. Conf. Wireless and Mobile Comput., Netw. and Commun. (WiMob)_ , 2012.
  * [140] H. Dahrouj and W. Yu, “Multicell interference mitigation with joint beamforming and common message decoding,” _IEEE Trans. Commun._ , vol. 59, no. 8, pp. 2264–2273, Aug. 2011.
  * [141] J. Zhang, J. Zhang, Y. Zhou, H. Ji, J. Sun, and N. Al-Dhahir, “Energy and spectral efficiency tradeoff via rate splitting and common beamforming coordination in multicell networks,” _IEEE Trans. Commun._ , vol. 68, no. 12, pp. 7719–7731, Dec. 2020.
  * [142] X. Su, Y. Yuan, and Q. Wang, “Performance analysis of rate splitting in $K$-user interference channel under imperfect CSIT: Average sum rate, outage probability and SER,” _IEEE Access_ , vol. 8, pp. 136 930–136 946, July 2020.
  * [143] D. Yu, J. Kim, and S. Park, “An efficient rate-splitting multiple access scheme for the downlink of C-RAN systems,” _IEEE Wireless Commun. Lett._ , vol. 8, no. 6, pp. 1555–1558, Dec. 2019.
  * [144] A. A. Ahmad, H. Dahrouj, A. Chaaban, A. Sezgin, and M. Alouini, “Interference mitigation via rate-splitting in cloud radio access networks,” in _Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC)_ , 2018.
  * [145] A. Alameer Ahmad, H. Dahrouj, A. Chaaban, A. Sezgin, and M. Alouini, “Interference mitigation via rate-splitting and common message decoding in cloud radio access networks,” _IEEE Access_ , vol. 7, pp. 80 350–80 365, June 2019.
  * [146] A. A. Ahmad, J. Kakar, R. Reifert, and A. Sezgin, “UAV-assisted C-RAN with rate splitting under base station breakdown scenarios,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2019.
  * [147] A. A. Ahmad, B. Matthiesen, A. Sezgin, and E. Jorswieck, “Energy efficiency in C-RAN using rate splitting and common message decoding,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2020.
  * [148] A. A. Ahmad, Y. Mao, A. Sezgin, and B. Clerckx, “Rate splitting multiple access in C-RAN,” in _Proc. IEEE Annu. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC)_ , 2020.
  * [149] A. A. Ahmad, H. Dahrouj, A. Chaaban, A. Sezgin, T. Y. Al-Naffouri, and M.-S. Alouini, “Power minimization via rate splitting in downlink cloud-radio access networks,” in _Proc. IEEE Int. Conf. Commun. (ICC) Workshop_ , 2020\.
  * [150] M. Z. Hassan, M. J. Hossain, J. Cheng, and V. C. M. Leung, “Device-clustering and rate-splitting enabled device-to-device cooperation framework in fog radio access network,” _IEEE Trans. Green Commun. Netw._ , vol. 5, no. 3, pp. 1482–1501, Sept. 2021.
  * [151] ——, “Energy-spectrum efficient content distribution in Fog-RAN using rate-splitting, common message decoding, and 3D-resource matching,” _IEEE Trans. Wireless Commun._ , vol. 20, no. 8, pp. 4929–4946, Aug. 2021\.
  * [152] R.-J. Reifert, A. A. Ahmad, Y. Mao, A. Sezgin, and B. Clerckx, “Rate-splitting multiple access in cache-aided cloud-radio access networks,” _Frontiers in Commun. and Netw._ , 2021.
  * [153] Z. Lin, M. Lin, T. de Cola, J.-B. Wang, W.-P. Zhu, and J. Cheng, “Supporting IoT with rate-splitting multiple access in satellite and aerial integrated networks,” _IEEE Internet Things J._ , vol. 8, no. 14, pp. 11 123–11 134, July 2021.
  * [154] S.-M. Tseng, G.-Y. Chen, and H.-C. Chan, “Cross-layer resource management for downlink BF-NOMA-OFDMA video transmission systems and supervised/unsupervised learning based approach,” _IEEE Trans. Veh. Technol._ , vol. 71, no. 10, pp. 10 744–10 753, 2022.
  * [155] L. Zhao, Y. Cui, Z. Liu, Y. Zhang, and S. Yang, “Adaptive streaming of 360 videos with perfect, imperfect, and unknown FoV viewing probabilities in wireless networks,” _IEEE Trans. Image Process._ , vol. 30, pp. 7744–7759, Aug. 2021.
  * [156] E. Piovano, H. Joudeh, and B. Clerckx, “On coded caching in the overloaded miso broadcast channel,” in _Proc. IEEE Int. Symp. Inf. Theory (ISIT)_ , 2017.
  * [157] E. Piovano, H. Joudeh, and B. Clerckx, “Generalized degrees of freedom of the symmetric cache-aided MISO broadcast channel with partial CSIT,” _IEEE Trans. Inf. Theory_ , vol. 65, no. 9, pp. 5799–5815, 2019.
  * [158] E. Demarchou, C. Psomas, and I. Krikidis, “Rate splitting with wireless edge caching: A system-level-based co-design,” _IEEE Trans. Commun._ , vol. 70, no. 1, pp. 664–679, Jan. 2022.
  * [159] J. Hachem, N. Karamchandani, S. Diggavi, and S. Moharir, “Coded caching for heterogeneous wireless networks,” _arXiv preprint arXiv:2006.01025_ , 2020\.
  * [160] E. Piovano, H. Joudeh, and B. Clerckx, “Overloaded multiuser MISO transmission with imperfect CSIT,” in _Proc. 50th Asilomar Conf. Signals, Syst. Comput._ , 2016.
  * [161] Y. Mao, E. Piovano, and B. Clerckx, “Rate-splitting multiple access for overloaded cellular internet of things,” _IEEE Trans. Commun._ , vol. 69, no. 7, pp. 4504–4519, July 2021.
  * [162] O. Dizdar and B. Clerckx, “Rate splitting multiple access for multi-antenna multi-carrier joint communications and jamming,” in _Int. Conf. in Sens. Signal Process. for Defence (SSPD)_ , 2021.
  * [163] ——, “Rate-splitting multiple access for communications and jamming in multi-antenna multi-carrier cognitive radio systems,” _IEEE Trans. Inf. Forensics Secur._ , vol. 17, pp. 628–643, Feb. 2022.