# IEEE 802.11be Multi-Link Operation: When the Best Could Be to Use Only a
Single Interface

Álvaro López-Raventós  Dept. Information and Communication Technologies
Universitat Pompeu Fabra (UPF)
Barcelona, Spain
<EMAIL_ADDRESS>Boris Bellalta  Dept. Information and Communication
Technologies
Universitat Pompeu Fabra (UPF)
Barcelona, Spain
<EMAIL_ADDRESS>
###### Abstract

The multi-link operation (MLO) is a new feature proposed to be part of the
IEEE 802.11be Extremely High Throughput (EHT) amendment. Through MLO, access
points and stations will be provided with the capabilities to transmit and
receive data from the same traffic flow over multiple radio interfaces.
However, the question on how traffic flows should be distributed over the
different interfaces to maximize the WLAN performance is still unresolved. To
that end, we evaluate in this article different traffic allocation policies,
under a wide variety of scenarios and traffic loads, in order to shed some
light on that question. The obtained results confirm that congestion-aware
policies outperform static ones. However, and more importantly, the results
also reveal that traffic flows become highly vulnerable to the activity of
neighboring networks when they are distributed across multiple links. As a
result, the best performance is obtained when a new arriving flow is simply
assigned entirely to the emptiest interface.

###### Index Terms:

WiFi 7, IEEE 802.11be, Multi-link Operation, Performance Evaluation, WLANs

##  I Introduction

Since its appearance back in the late ’90s, Wi-Fi has been continuously
innovating to adapt its performance to the new user demands. Nowadays, the
appearance of modern applications are pushing wireless local area networks
(WLANs) to their performance limits again. For instance, remote office, cloud
gaming and virtual and augmented reality (VR&AR) are straight-forward examples
that not only demand high-throughput but reliable and low-latency
communications [1]. To that end, in may 2019, the Institute of Electric and
Electronic Engineers (IEEE) established a Task Group (TG) to address and
design a new physical (PHY) and medium access control (MAC) amendment, known
as IEEE 802.11be Extremely High Throughput (EHT).

The IEEE 802.11be EHT seeks to further increase the throughput performance,
reduce the end-to-end latency and increase the reliability of communications
[2]. To do so, different technical features have been suggested in both PHY
and MAC layers [3]. Regarding PHY layer, TGbe propose the complementary
adoption of the 6 GHz band, empowering the use of wider bandwidth channels up
to 320 MHz, additionally to a new 4096 QAM high-order modulation (i.e., 12
bits per symbol), as immediate approaches to increase Wi-Fi peak-throughput.
Besides, the multiple-input multiple-output (MIMO) capabilities are also being
revised to upgrade them to support up to 16 spatial streams, introducing also
an implicit channel sounding procedure. Despite those enhancements, it is not
in the PHY, but in the MAC layer where we can find the most disruptive
updates. We refer to the adoption of multi-link communications, which
represents a paradigm shift towards concurrent transmissions. Although under
the multi-link label we find the multi-AP coordination and the multi-
band/multi-channel operation features, this article is focused on the analysis
of the latter one.

Upon its current version, the IEEE 802.11 standard already defines two MAC
architectures for supporting the multi-band/multi-channel operation. However,
both designs present a common limitation: MAC service data units (MSDUs)
belonging to the same traffic flow can not be transmitted across different
bands [4]. As a result, stations are tied to a single band, preventing a
dynamic and seamless inter-band operation. That is, one link is selected
between transmitter and receiver to carry out data exchanges, remaining the
other unused [5, 6, 7, 8]. To benefit from the fact that modern APs and
stations incorporate dual, or even, tri-band capabilities, TGbe is working on
developing the multi-link operation111Throughout this paper, we will refer to
the multi-band/multi-channel operation feature as the MLO, following the
notation of the TGbe. (MLO) framework to allow concurrent data transmission
and reception in different frequency channels/bands.

MLO allows APs and stations to exploit the fact of having multiple radio
interfaces to transmit and receive data. However, how to use them to maximize
the WLAN performance, i.e., how to properly distribute the traffic across the
multiple interfaces, is still an open question. Therefore, in this paper, we
tackle such question by considering different traffic balancing policies. The
obtained results show the advantages of coordinating the available interfaces
through the MLO framework, as well as provide some insights on how to
distribute the traffic across them. It is worthy to anticipate our conclusion
that distributing the traffic over multiple interfaces may not be always the
best solution.

##  II Multi-link operation: an overview

The introduction of MLO is a breaking point for Wi-Fi, as its adoption
represents a paradigm shift towards multi-link communications. However, this
implementation involves new challenges in terms of APs’ and stations’
architecture design, transmission modes, channel access, and management.

To enable a concurrent operation across multiple interfaces, the existing
multi-band MAC architecture have been exhaustively revised. In this context,
TGbe introduces the concept of multi-link capable device (MLD), which consists
of a single device with multiple wireless PHY interfaces that provides a
unique MAC instance to the upper layers. Such implementation is achieved by
dividing the MAC sub-layer in two parts [9]. First, we find the unified upper
MAC (U-MAC), which is the common part of the MAC sub-layer for all the
interfaces. Apart from performing link agnostic operations such as A-MSDU
aggregation/de-aggregation and sequence number assignment, the U-MAC
implements a queue that buffers the traffic received from the upper-layers.
That is, traffic awaits in the U-MAC before it is assigned to a specific
interface to be transmitted. Also, the U-MAC provides some management
functions such as multi-link setup, association and authentication. On the
other hand, there is the independent low MAC (L-MAC), an individual part of
the MAC sub-layer for each interface that performs link specific
functionalities. There, we find the link specific EDCA queues (one for each
access category) that hold the traffic until its transmission. Also,
procedures such as MAC header and cyclic redundancy check (CRC)
creation/validation, in addition to management (e.g., beacons) and control
(e.g., RTS/CTS and ACKs) frame generation are implemented at the L-MAC [10].

This two-tier MAC implementation enables frames to be simultaneously
transmitted over multiple links, as the U-MAC performs the allocation and
consolidation of MPDUs when acting as transmitter and receiver, respectively.
Also, it enables seamless transitions between links minimizing the access
latency, and addressing an efficient load balancing.

At a link level (i.e., in the L-MAC), channel access takes place. In current
proposals, TGbe defines different channel access methods accordingly to two
different transmission modes: asynchronous and synchronous. First, there is
the asynchronous transmission mode. Under this operational mode, a MLD can
transmit frames asynchronously on multiple links, while keeping for each one
its own channel access parameters (e.g., contention window (CW), arbitration
inter-frame spacing number (AIFSN), etc.). That is, each link has its own
primary channel, independent of the others. Also, this transmission mode
allows the simultaneous transmission and reception (STR) capability over
multiple links, enabling concurrent uplink and downlink communications. Such
implementation has been proved to minimize latency [11], while maximizing the
throughput [12]. Ideally, the asynchronous mode should be selected as
operational scheme, however, a MLD may not be STR-capable due to the in-device
coexistence (IDC) interference. This issue is caused by an excessive power
leakage between interfaces that do not have sufficient frequency separation in
their operating channel (e.g., two channels in the 5 GHz band). As a result,
IDC prevents frame reception on one interface, during an ongoing transmission
on the other interface. To avoid the IDC issue, TGbe proposes the synchronous
mode, which relies on synchronized frame transmissions across the available
interfaces. With that, APs or stations are prevented to perform an STR
operation, avoiding IDC problems but at the cost of a lower network throughput
[13, 14]. Under this operation mode, the channel access can be performed
either following the single primary channel (SPC) or the multiple primary
channel (MPC) methodology. Basically, the SPC performs contention on a unique
channel, whereas in the MPC contention is performed on all channels. Either
using the SPC or the MPC method, if one channel wins contention the others are
checked during a PCF inter-frame space (PIFS) time to see if they can be
aggregated. As a result, channels in idle state are aggregated and a
synchronous transmission starts [15]. It is worth mention that AP MLDS may
change its transmission modes (e.g., asynchronous to synchronous, and vice
versa) at any time. Devices operating in a synchronous mode are referred to as
constrained MLDs, or non-STR MLDs, since they are not allowed to transmit
through an idle interface at the same time they are receiving through another.

Regarding MLO feature management, the TGbe proposes the multi-link setup
process. Instead of designing new management frames, TGbe agreed to reuse the
current association request/response frames by adding an extra multi-link
element or field. That is, the setup (i.e., MLO capability exchange) is
performed jointly with the association mechanism. Then, through the multi-link
element, AP MLDs and non-AP MLD negotiate and establish their subsequent
operation scheme by exchanging their capabilities. For instance, they exchange
information regarding the number of supported links, their ability to perform
STR, their transmission operation (asynchronous or synchronous), and other
per-link information (e.g., frequency band, supported bandwidth, number of
spatial streams, etc.) [16, 17]. To reduce overhead, the described multi-link
setup process is proposed to be performed only on a single link, which,
indeed, will be the lowest in frequency due to propagation constraints.

The interface usage negotiation is performed by measuring link qualities at
all interfaces. That is, those receiving a quality value above the clear
channel assessment (CCA) threshold are set as enabled, while disabled
otherwise. Hence, for each enabled interface, we will have an enabled link. It
is worth mention that, as users may keep themselves mobile, any link listed as
disabled may be added afterwards to the set of enabled links, by requesting a
re-setup.

##  III System model

###  III-A Node placement

We consider a set of IEEE 802.11be MLO-capable WLANs with $N$ APs and $M$
stations. Over a given area, APs are placed uniformly at random. However, we
only accept the generated scenario as valid if the inter-AP distance is higher
than 5 m to avoid unrealistic overlaps. Otherwise, we discard it and generate
a new one. Then, we decide the number of stations that will be associated to
each AP, placing them around it at a distance $d$ within the interval
[$1$-$8$] m and an angle $\theta$ between [0-2$\pi$], both selected uniformly
at random.

Regardless of its type, APs and stations are configured with 3 wireless
interfaces (i.e., $i_{1}$, $i_{2}$ and $i_{3}$), each one using 2 SU-MIMO
spatial streams. Additionally, we consider that each interface operates at a
different frequency band. That is, for each AP MLD interface, one channel $c$
is selected uniformly at random from the set of channels of each band, where
$\mathcal{C}_{b}$ is the set of available channels of band $b$. Thus,
${c_{i_{1}}\hskip 0.5pt\in\hskip 0.5pt\mathcal{C}_{2.4}}$, ${c_{i_{2}}\hskip
0.5pt\in\hskip 0.5pt\mathcal{C}_{5}}$ and ${c_{i_{3}}\hskip 0.5pt\in\hskip
0.5pt\mathcal{C}_{6}}$. The different set of channels are detailed in Table I.
Note that depending on the band, different channel widths are considered.

###  III-B MLO capabilities

The stations’ set-up, as well as the establishment of the enabled interfaces,
is performed through the 2.4 GHz link. Link qualities are exchanged, and
therefore, interfaces that meet the quality criteria (i.e., above CCA
threshold) are set as enabled, while disabled otherwise. In essence, this
setup relies on a simplified version of the MLO setup process described in
Section II. Additionally, we assume that all nodes perform an asynchronous
channel access for each of their enabled interfaces, while their default
policy is set as Multi Link Same Load to All interfaces (MLSA) (see Section
III-E), which will be our baseline evaluation policy unless stated
differently.

Figure 1: Policy implementation. From left to right: SLCI, MLSA and MCAA
representation. Under the wireless medium representation, the gray area
corresponds to the channel occupancy already seen by the MLD at the given
interface, which can be from its neighbor APs, as well as its own ongoing
flows.

###  III-C Channel model and data rate selection

Path loss is characterized following the IEEE 802.11ax enterprise model for a
single floor environment. The path loss between an station $i$ and AP $j$ is
given by

$\centering\small\begin{split}\text{PL}(d_{i,j})&=40.05+20\log_{10}\left(\frac{f_{c}}{2.4}\right)+20\log_{10}(\text{min}(d_{i,j},d_{bp}))\\\
&+(d_{i,j}>d_{bp})\cdot
35\log_{10}\left(\frac{d_{i,j}}{d_{bp}}\right)+7W_{i,j}\end{split}\@add@centering$

where $f_{c}$ is the AP’s carrier frequency in GHz, $d_{i,j}$ is the distance
between station $i$ and AP $j$ in meters, $d_{bp}$ is the breaking point
distance in meters, and $W_{i,j}$ are the number of traversed walls. We set
the breaking point distance to 5 m and the number of traversed walls to 4.
Note that the resulting propagation losses are given in dB.

The Modulation and Coding Scheme (MCS) used by each interface is selected
according to the signal-to-noise ratio (SNR) between the AP and the stations.
For instance, an station’s interface $i$, with 2 spatial streams and a SNR of
11 dB, will achieve a data rate of 243.8 Mbps, using modulation 1024-QAM with
a coding rate of 5/6 and guard interval of 3.2 $\mu$s in a 20 MHz channel.

TABLE I: Evaluation setup

Parameter | Description
---|---
Carrier frequency | Depends on channel selection
$\mathcal{C}_{2.4}$ channel set | 1 (20 MHz), 6 (20 MHz), 11 (20 MHz)
$\mathcal{C}_{5}$ channel set | 38 (40 MHz), 46 (40 MHz), 58 (80 MHz)
$\mathcal{C}_{6}$ channel set | 55 (80 MHz), 71 (80 MHz), 15 (160 MHz)
System bandwidth | 60 MHz/160 MHz/320 MHz
AP/STA TX power | 20/15 dBm
Antenna TX/RX gain | 0 dB
CCA threshold | -82 dBm
AP/STA noise figure | 7 dB
Single user spatial streams | 2
MPDU payload size | 1500 bytes
Path loss |  Same as [18]
Avg. flow duration |  T${}_{\text{on}}=$ 1 s
Avg. flow interarrival time |  T${}_{\text{off}}=$ 3 s
Min. contention window | 15
Packet error rate | 10%
Simulation time | 120 s (1 per simulation)
Number of simulations | 100 (per evaluation point)

###  III-D Traffic generation and CSMA operation

Only downlink traffic is considered. The traffic directed to each station is
modeled as an on/off Markovian model. In the on period, the AP receives a
Constant Bit Ratio (CBR) traffic flow of $\ell$ Mbps, whereas zero during the
off period. Both on and off periods are exponentially distributed with mean
duration T${}_{\text{on}}$ and T${}_{\text{off}}$, respectively. Either
T${}_{\text{on}}$ and T${}_{\text{off}}$ have been set to resemble a mixture
of different types of traffic as in real scenarios. The value of the two
parameters is presented in Table I. We refer to the traffic generated during
the on period as a traffic flow, and to $\ell$ as the required bandwidth.

Regarding the CSMA/CA operation, it follows the abstraction presented in [18],
which considers the aggregate channel load observed by each AP to calculate
the airtime that can be allocated to each flow. Although the considered
abstraction does not capture low-level details of the PHY and MAC layers
operation, it maintains the essence of the CSMA/CA: the ’fair’ share of the
spectrum resources among contending APs and stations. In addition, this
presented abstraction allows us to simulate larger networks, and larger
periods of time, and so study the network performance at the flow-level.

###  III-E Traffic allocation policies

When a new traffic flow arrives at the AP, the traffic manager determines how
it is distributed over the different enabled interfaces of each station. The
three traffic allocation policies considered in this work are:

  * •

Single Link Less Congested Interface (SLCI): upon a new flow arrival, pick the
less congested interface and assign it to the incoming flow.

  * •

Multi Link Same Load to All interfaces (MLSA): upon a new flow arrival,
distribute the incoming traffic flow equally between all the enabled
interfaces of the receiving station. That is, let $\ell$ be the required
bandwidth of the incoming flow, and $N_{\rm int}$ the number of enabled
interfaces in the destination station. This is, traffic allocation per
interface is given by: $\ell_{i}=\ell/N_{int}$, with $\ell_{i}$ the bandwidth
allocated to interface $i$.

  * •

Multi Link Congestion-aware Load balancing at flow arrivals (MCAA): upon a new
flow arrival, distribute the incoming traffic flow accordingly to the channel
occupancy observed at the AP considering the enabled interfaces at the
receiving station. That is, let $\rho_{i}$ the percentage of available (free)
channel airtime at interface $i$. Then, the required bandwidth allocated to
interface $i$ is given by
$\ell_{i\in\mathcal{J}}=\ell\frac{\rho_{i}}{\sum_{\forall
j\in\mathcal{J}}{\rho_{j}}}$, with $\mathcal{J}$ the set of enabled interfaces
at the target station.

While MLSA allocates traffic regardless of the congestion level of each
interface, the other policies take it into account. When a new flow arrives,
SLCI evaluates the current congestion level of each interface to identify the
emptiest one, and so, it assigns the whole traffic flow to it. Then, we find
MCAA, which tries to maximize the spectrum used by balancing the traffic
allocated to the different interfaces taking into account their current
occupancy. Under the MCAA, for instance, a flow with a bandwidth requirement
of 10 Mbps, and capable to be distributed across three interfaces with
$\rho_{1}=0.2$, $\rho_{2}=0.6$, and $\rho_{3}=0.5$, will be split as follows:
1.54 Mbps allocated to the interface 1, 4.61 Mbps to the interface 2, and 3.85
Mbps to interface 3. Although this approach seems very convenient, traffic may
become more vulnerable to contention with external networks, due to the fact
of being spread across multiple interfaces. Figure 1 shows the different
policies presented.

(a)

(b)

(c)

Figure 2: Controlled scenario evaluation. From left to right: a) scenario
representation, b) average channel occupancy for AP B, and c) aggregate
throughput performance. The high, medium and low shaded areas represent the
operation range for the 6 GHz, 5 GHz and 2.4 GHz bands, respectively.

##  IV Performance evaluation

To assess the performance evaluation of the MLO, we use the Neko222The Neko
simulation platform can be found in GitHub at: https://github.com/wn-upf/Neko
simulation platform.

###  IV-A A controlled scenario

First, we focus our evaluation on the comparison between a MLO-capable
deployment against a traditional multi-band single link (SL). To do so, we
consider a controlled scenario to pinpoint the potential benefits about the
introduction of MLO, while identifying some pitfalls without the complex
interactions that random deployments may originate. Figure 2(a) depicts a
typical controlled WLAN deployment, in which three basic service sets (BSSs)
are placed inline. Under such scenario, we assess the network behavior under
two different cases:  i) high traffic demands, and  ii) high station density
deployments. For each evaluation point considered in each case, we simulate
100 scenarios in which stations’ positions are newly generated following the
stated in Section III-A.

To begin with, we characterize the network performance for different traffic
loads. In this regard, we fixed the average number of associated stations per
AP to 20, while increasing the required bandwidth for each flow from 1 Mbps to
8 Mbps. That is, 800 total simulated scenarios, as we evaluate 8 traffic load
ranges for 100 different scenarios. Besides, the whole deployment is
considered to be either multi-band SL (i.e., 3 SL APs), or MLO-capable (i.e.,
3 AP MLDs). In both scenarios, the channel configuration is the same for the 3
APs, whereas the other simulation parameters are kept as presented in Table I.
It is worth mention that when using the multi-band SL approach, stations
select the interface to be attached to in a random fashion from the set of
enabled interfaces. For MLO, the configuration of each interface is set as
stated in Section III-A, and both APs and stations use baseline MLSA policy.

Figure 2(b) shows the experienced channel occupancy by AP${}_{\text{B}}$. We
observe that AP${}_{\text{B}}$ suffers from the well-known flow-in-the-middle
effect in its 2.4 GHz interface, due to AP${}_{\text{A}}$ and
AP${}_{\text{C}}$ keeping the wireless medium in busy state for most of their
time. Consequently, traffic bandwidth requirements in this interface are only
met for low level demands in both approaches, since AP${}_{\text{B}}$ will
start to drop data traffic as soon as traffic load increases. On the contrary,
such effect is alleviated in 5 GHz and 6 GHz links as a consequence of nodes’
spatial distribution, which provides all APs with two downlink contention-free
links (i.e., only the 2.4GHz link satisfies the energy detection threshold).
Although such condition may benefit the MLO capability implementation, in this
case it is wasted by not using a congestion-aware traffic allocation policy to
leverage both contention-free links. Such issue has a negative impact over
AP${}_{\text{B}}$ performance, as its experienced occupation is nearly the
same either when using both multi-band SL or MLO approaches. That is, there is
no difference between spreading in the same proportion the traffic over the
different links through MLO, than balancing and/or steering clients across
links, like current multi-band SL deployments do. Thus, it is easy to conclude
that without a proper traffic allocation policy, MLO underperforms.

Similarly, Figure 2(c) shows the aggregate throughput of each AP when the
number of stations per BSS increases. Differently from the previous case, we
set now that the required bandwidth for each generated flow ${\ell}$ is
selected uniformly from the 2 Mbps to 8 Mbps interval (i.e.,
${\ell}\sim\textit{U}[2,8]$). The other simulation parameters are kept as
previously. Now, we introduce the SLCI congestion-aware policy presented in
Section III-E to provide a comparison between a non-congestion and a
congestion aware policy. From the figure, we observe how the MLSA policy
struggles to cope even with 15 stations per BSS. For instance,
AP${}_{\text{B}}$ already registers throughput losses around 12% for 15
stations. On the contrary, the SLCI assisted MLO operation supports a higher
number of stations, while AP${}_{\text{B}}$ only experiences a 5% throughput
losses when considering 20 stations. Additionally, we observe that the flow-
in-the-middle effect is reduced by the implementation of a congestion-aware
policy. For instance, we observe that AP${}_{\text{B}}$’s aggregate throughput
is reduced 10% in comparison to its neighbors when considering MLSA for 20
stations, whereas only a 4% when considering SLCI.

###  IV-B Random deployments

In order to get more insights with respect to the performance of the different
policies presented in Section III-E, we conduct a set of simulations in random
generated scenarios. Similarly to the previous case, we address two different
cases:  i) high user demands, and  ii) high AP density  deployments. Both
cases are conducted over an area of 45x45$\text{m}^{2}$, with nodes being
placed and configured as stated in Section III. Besides, we consider that both
APs and stations within the evaluation area implement the same policy. Other
simulation conditions are kept as stated in Table I. For each evaluation point
considered in each case, we simulate 100 different scenarios in which APs and
stations’ positions are generated as stated in Section III-A.

First, we conduct the evaluation with respect to stations traffic
requirements. For this purpose, we locate 10 AP MLDs within the considered
area, as well as a random number of stations (i.e., $M\sim U[15,25]$) for each
AP MLD. Then, we carry out simulations by gradually increasing the required
bandwidth ${\ell}$ of each incoming flow from 1 Mbps to 8 Mbps. To evaluate
the performance of the network we use the satisfaction $s$ metric. We define
the satisfaction as the ratio between the required airtime to achieve the
required flow bandwidth, and the actual amount that can be allocated. This
metric is calculated for each AP, and then, $\overline{s}$ indicates the
average satisfaction of the network computed as the sum of the average
satisfaction experienced by each AP divided by the total number of APs in the
WLAN. Figure 3 shows the probability that the whole WLAN achieves an average
satisfaction $\overline{s}$ greater or equal than 95%. As expected, the
baseline MLSA operation is outperformed by the rest of policies, due to the
fact that traffic allocation is no longer allocated blindly, but following a
congestion-aware approach. In this regard, we find that both SLCI and MCAA
congestion-aware policies are capable to deal with higher traffic loads
without losing performance. For instance, for ${\ell~{}=~{}5~{}\text{Mbps}}$,
SLCI and MCAA are able to provide excellent satisfaction values in 92% and 88%
of the scenarios evaluated, respectively, whereas MLSA only manages to get
good results in 5% of them. Finally, notice that SLCI works better for medium
load ranges, while MCAA works better for high loads as its ability to
distribute traffic across the different interfaces results in slightly higher
satisfaction values.

Figure 3: Prob. of average satisfaction TABLE II: Traffic allocation
efficiency

bandwidth req. per flow | Policy
---|---
MLSA | SLCI | MCAA
2 Mbps | 0.996 | 1.00 | 1.00
4 Mbps | 0.925 | 0.989 | 0.985
6 Mbps | 0.830 | 0.931 | 0.930
8 Mbps | 0.750 | 0.833 | 0.842

To further examine the obtained results, we show in Table II the traffic
allocation efficiency. We define this efficiency as the ratio between the
required bandwidth of each flow, and the actual throughput, for each of the
policies. Once more, we can see that congestion-aware policies are able to
outperform the baseline operation, as all of them can effectively allocate
more than the 90% of the required bandwidth per flow for values between 2 Mbps
and 6 Mbps. Also, notice how the performance of the two congestion-aware
policies (i.e., SLCI and MCAA) is highly similar.

Finally, we assess the performance of the different policies under high AP
density deployments. To do so, we gradually increase the number of APs, to
generate four different density deployments: low ($N=5$), medium ($N=10$),
med-high ($N=20$) and high ($N=40$). For each AP, we placed a random number of
stations (i.e., $M\sim U[15,25]$). Figure 4 shows the empirical cumulative
distribution function (CDF) of the average throughput drop ratio experienced
for each density. The throughput drop ratio represents the percentage of
traffic that cannot be served. Then, it is computed as one minus the ratio
between the achieved throughput and the required one. As expected, the
probability of having higher drop ratio values increases upon network
densification. However, the different congestion-aware policies are able to
reduce significantly those values. For instance, for medium density
deployments, we find that drop ratio is decreased 2.25x by means of SLCI
implementation for the 75th percentile compared to MLSA. Additionally, we find
that SLCI further improves MCAA as the density increases. This phenomena
occurs as MCAA policy allocates traffic to more interfaces, which in fact
increases the exposure to the dynamics of the other APs. That is, in high
density deployments, APs tend to have more neighboring APs, and so, the use of
a high number of interfaces to transmit makes the traffic much more vulnerable
as the probability of having a channel overlap increases.

Figure 4: Drop ratio CDF for different network densities

##  V Conclusions & Future Work

In this paper, we assessed and evaluated the adoption of multi-link
communications by the upcoming IEEE 802.11be amendment. Through a wide variety
of scenarios, our results show that the implementation of MLO can help the
next generation Wi-Fi networks to satisfy the highly demanding requirements of
modern applications. However, we show that the performance of MLO depends
mainly on the implemented traffic allocation policy. From that side, as
expected, congestion-aware allocation policies able to adapt to the
instantaneous state of the network are the best performing ones. Additionally,
we have observed that allocating an incoming flow to the emptiest interface is
almost as good, if not better, than proportionally distributing the flow over
multiple interfaces. Such conclusion relies on the fact that a single
interface policy not only reduces traffic exposure, but minimizes the
complexity of the traffic allocation procedure.

Regarding future work, we consider three potential extensions to further
improve MLO operation. First, extend MCAA to re-allocate the traffic over the
different interfaces periodically. Indeed, such implementation may minimize
the negative impact of the activity of neighboring networks in terms of
performance, but at the cost of entailing a much complex policy structure.
Second, throughout this paper, we only considered IEEE 802.11be MLO capable
networks, whereas in real scenarios they may coexist with legacy single link
networks. Therefore, it may be relevant to study the different MLO traffic
allocation policies under those conditions. Third, and last, to integrate the
MLO operation in a Software Defined Networking framework [19] to orchestrate
the different APs that belong to the same WLAN, as well as to design and
evaluate centralized multi-AP MLO policies.

## Acknowledgments

This work has been partially supported by the Spanish Government under grant
WINDMAL PGC2018-099959-B-I00 (MCIU/AEI/FEDER,UE), by the Catalan Government
under grant 2017-SGR-1188, and by a Gift from Cisco (2020).

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# Analogue Radio Over Fiber for Next-Generation RAN: Challenges and
Opportunities

Yichuan Li, Qijie Xie, Mohammed El-Hajjar, Senior Member, IEEE, Lajos Hanzo,
Fellow, IEEE
The financial support of of the China Postdoctoral Science Foundation under
Grant No. 2019TA0209, the EPSRC projects EP/N004558/1, EP/L018659/1 as well as
of the European Research Council’s Advanced Fellow Grant under the Beam-Me-Up
project and of the Royal Society’s Wolfson Research Merit Award is gratefully
acknowledged.Yichuan Li is with Harbin Institute of Technology (Shenzhen),
Shenzhen, China (e-mail: liyichuan@hit.edu.cn)Qijie Xie is with Pengcheng
Laboratory (xieqj@pcl.ac.cn)M. El-Hajjar, and L. Hanzo are with the School of
ECS, University of Southampton, SO17 1BJ, United Kingdom (
<EMAIL_ADDRESS>lh@ecs.soton.ac.uk).

###### Abstract

The radio access network (RAN) connects the users to the core networks, where
typically digitised radio over fiber (D-RoF) links are employed. The data rate
of the RAN is limited by the hardware constraints of the D-RoF-based backhaul
and fronthaul. In order to break this bottleneck, the potential of the
analogue radio over fiber (A-RoF) based RAN techniques are critically
appraised for employment in the next-generation systems, where increased-rate
massive multiple-input-multiple-output (massive-MIMO) and millimeter wave
(mmWave) techniques will be implemented. We demonstrate that huge bandwidth
and power-consumption cost benefits may accrue upon using A-RoF for next-
generation RANs. We provide an overview of the recent A-RoF research and a
performance comparison of A-RoF and D-RoF, concluding with further insights on
the future potential of A-RoF.

###### Index Terms:

Optical fiber, radio access network, digitised radio over fiber, analogue
radio over fiber, MIMO.

##  I Introduction

Figure 1: RAN Evolution (RRH: Remote Radio Head, BBU: Baseband Unit, AT:
Antenna, CU: Central Unit, DU: Distributed Unit, eCPRI: enhanced Common Public
Radio Interface.)

Radio access networks (RAN) have been evolving throughout the past five
wireless generations. In the fourth Generation (4G) era, the RAN architecture
typically relied on the concept of centralised RANs (C-RAN), where the central
unit (CU) has several baseband units (BBUs) and can be connected to several
remote radio heads (RRHs) by fiber, where each RRH supports an individual cell
[1]. The service-oriented fifth Generation (5G) evolved further by relocating
some functions of the CU to the distributed unit (DU) for the sake of flexibly
serving all use-cases, while some physical-layer functions were moved to the
RRH connected by digitised radio over fiber (D-RoF) links [2, 3].

As a mainstream 4G C-RAN fronthaul technique, D-RoF supports high-integrity
data transmission by relying on sampled and quantized radio frequency (RF)
signals over fiber, while dispensing with digital signal processing in the
RRHs. However, following the emergence of high-rate massive multiple-input-
multiple-output (massive-MIMO) and millimeter Wave (mmWave) communication
techniques, the fully centralised D-RoF based 4G RAN architecture, where only
radio functions are performed at the RRHs [2], requires hundreds of Gigabit
per second (Gbps) data rates between the CU and the RRH.

To meet these escalating capacity demands, the enhanced Common Public Radio
Interface (eCPRI) design [4] of Fig 1 can be employed for data transmission
over the fiber using the existing D-RoF techniques by restricting the 5G
fronthaul data rate below 30 Gb/s. However, this is achieved at the cost of
increasing the complexity of RRHs [4] by assigning them more complex tasks.
Furthermore, the sampling and quantization process of D-RoF increases the
bandwidth requirement of the fronthaul fiber-link in line with the quantizer’s
resolution. Hence the question arises whether analogue radio over fiber
(A-RoF) might be able to circumvent this problem [5, 6] by dispensing with
quantization all together, and if so-at what price. Indeed, there are some
existing A-RoF implementations in the C-RAN systems, which we will elaborate
on in Section V. These techniques are capable of supporting the 5G rate, but
require further improvements for next-generation systems relying on increased-
rate massive-MIMO and mmWave solutions due to the susceptibility of A-RoF to
the fiber-links’s nonlinearity and dispersion [6].

Against this backdrop, we present the design of fully centralised A-RoF aided
C-RANs for supporting high-rate applications. Explicitly, we critically
appraise the family of A-RoF aided RAN systems as a low-cost, low-power
solution. Hence a suite of advanced fiber-link impairment mitigation
techniques will be suggested in the context of high-bandwidth, high-order
modulation based massive-MIMO solutions.

The rest of this paper is organised as follows. Section II provides a brief
historical perspective on the radio access network’s evolution. Then, we
discuss both the bandwidth and the power consumption reduction of A-RoF
compared to D-RoF in Sections III and IV, while the current status of A-RoF
aided 5G applications is presented in Section V. Finally, we provide an
outlook in Section VI, followed by our conclusions in Section VII.

Figure 2: Left: A-RoF, Right: D-RoF. (DSP: Digital Signal Processing, EBPF:
Electronic Bandpass Filter, EA: Electronic Amplifier, UE: User End.)

##  II A brief history of the Radio Access Network Evolution

As shown in Fig. 1, the 2G cellular network relied on the macro base-station
(BS) concept, where all the equipment except for the antennas was placed in a
cell tower, with the copper-connected antennas being placed at the top of each
tower [1]. Then, to reduce the RF transmission attenuation, the radio unit or
RRH was moved to be near the antenna in the distributed 3G RAN, where
separating the radio- and baseband-functions led to the concept of fronthaul
networks, as shown in Fig. 1 [1]. Subsequently, in the 4G era, the C-RAN
concept was born, where a single CU accommodating several baseband units is
connected to multiple RRH by fiber. This architecture substantially reduces
the operational expenditure in terms of the management and cooling cost, due
to the small size of each cell tower [1]. The C-RAN architecture utilised
D-RoF techniques in the fronthaul network for the sake of eliminating most of
the electronic processing in the RRHs, such as MIMO processing and up-
conversion. Subsequently, the 5G RAN at the right of Fig. 1 adopted the
functional split concept, where some electronic functions such as modulation
are moved to the RRH to relieve the bandwidth requirement imposed on the
fiber-link fronthaul, where the fronthaul standard employed is the eCPRI. The
evolution of the RAN has been governed by its bandwidth requirement,
especially in the context of massive-MIMO and mmWave communications. As
mentioned in Section I, A-RoF has a much lower bandwidth requirement and power
consumption of the D-RoF based fronthaul, because the latter expands the
bandwidth in line with the number of quantization bits. Hence in this paper we
critically appraise the potential of A-RoF as a compelling RAN solution [2, 3]
by comparing the A-RoF and D-RoF both from a bandwidth-efficiency and power-
consumption perspective.

##  III A-RoF versus D-RoF: Bandwidth Efficiency Analysis

In Fig. 2, we compare the A-RoF based C-RAN system to its D-RoF counterpart,
when transmitting a $N_{t}\times N_{r}$ MIMO signal, with $N_{t}$ and $N_{r}$
representing the number of transmit and receive antennas. In the A-RoF, no
analogue-to-digital conversion (ADC) and digital-to-analogue conversion (DAC)
is required. In the A-RoF’s CU, the data bits are modulated and subjected to
MIMO processing, followed by up-conversion. The resultant signal is then
electronic-to-optical (E/O) converted, as seen in Fig. 2. The wavelength-
division-multiplexing multiplexer (WDM-mux) generates a WDM signal [7], where
each wavelength carries its individual RF signal over the optical fiber to the
RRHs of Fig. 2. In the A-RoF’s RRH of Fig. 2, the WDM signal is fed into the
WDM demultiplexer (WDM-demux). Then, each wavelength is optical-to-electronic
(O/E) converted for recovering the RF signals using photo-detectors (PD).
Finally, after bandpass filtering and electronic amplification, the RF signals
carried by each wavelength of the WDM signal are mapped to each antenna for
wireless MIMO transmission, where the MIMO signal processing was carried out
in the CU. By contrast, in D-RoF, the RF signal is sampled and quantized prior
to the E/O conversion, where the resultant digital signal is carried by the
optical signal over the fiber. More explicitly, the bandwidth of the D-RoF
signal is expanded by the sampling and quantization of the RF signal [6].

###  III-A Bandwidth Saving by A-RoF

Consider a MIMO system employing $N_{t}$ transmit antennas (TA) and $N_{r}$
receive antennas (RA) implemented in each 360${}^{\circ}/M$ wide sector of
each RRH, where the number of sectors is $M$.

In the case of A-RoF, we assume that the baseband bandwidth is $W_{bb}$. Then,
upon assuming that each TA has its own dedicated WDM carrier and each of the
$M$ sectors also has the same bandwidth per carrier, we have a double-sided
bandwidth of $2W_{bb}$ after RF carrier modulation, yielding a total A-RoF
bandwidth of $W=2W_{bb}N_{t}M$. In other words, the aggregated bandwidth
represents the total bandwidth of all the wavelength $N_{t}$ and of all the
sectors $M$ involved in a WDM system. Furthermore, according to Nyquist’s
theorem [8], the corresponding A-RoF bit rate would be
$2W_{bb}N_{t}M\log_{2}(S)$, where $S$ is the number of modulation
constellation points [9].

In the case of D-RoF systems, when bandpass sampling is invoked [9], the
sampling rate $f_{s}$ can be expressed as follows [8]:

$\begin{array}[]{lcl}(2f_{max})/n_{z}\ \leq
f_{s}\leq(2f_{min})/(n_{z}-1),\end{array}$ (1)

where $1\leq n_{z}\leq|f_{max}/(f_{max}-f_{min}\ )|$ and
$f_{max}=f_{c}+W_{bb}$ represents the maximum frequency edge of the RF signal,
while $f_{min}=f_{c}-W_{bb}$ is its minimum frequency edge and $f_{c}$ is the
carrier frequency.

To elaborate further on the D-RoF system, the minimum new bit rate after the
resampling and quantization of the RF signal becomes $D=f_{s}RN_{t}M(2)C_{w}C$
[8], where $R$ is the quantizer’s sampling resolution, the factor 2 is the
multiplicative factor for the I/Q data, $C_{w}$ is the control signalling rate
carrying the control and management information and C is the coding rate of
the line coding employed. Then, if perfect rectangular pulse shaping is used,
the D-RoF bandwidth is $D/2$.

As a result, we arrive at the bandwidth ratio $B$ and the bit rate ratio $C$
between the D-RoF and A-RoF as:

$\begin{split}\begin{array}[]{lcl}B=\frac{D/2}{W}=\frac{f_{s}RN_{t}M(2)C_{w}C/2}{2W_{bb}N_{t}M}=\frac{f_{s}RC_{w}C}{2W_{bb}}\\\
\text{and}\\\
C=\frac{D}{2W_{bb}N_{t}M\log_{2}(S)}=\frac{f_{s}RN_{t}M(2)C_{w}C}{2W_{bb}N_{t}M\log_{2}(S)}=\frac{f_{s}RC_{w}C}{W_{bb}\log_{2}(S)}.\end{array}\end{split}$
(2)

Next, we report on an experiment, in which a stream of 256 quadrature
amplitude modulated (QAM) symbols are carried by a 28 GHz RF carrier
supporting a $N_{t}=16$ MIMO system, where the number of 120∘ sectors per cell
is $M=3$. Furthermore, we used a sampling resolution of $R=15$ for D-RoF
digitisation, as well as a line-coding rate of $C_{w}=16/15$ and $C=10/8$,
which follow the CPRI-based fronthaul specification [4, 10].

In Fig. 3, we show the data rate transmitted both by A-RoF and D-RoF, when the
bandwidth per wavelength ranges from 50 MHz to 1 GHz. As shown in Fig. 3, the
achievable data rate of the A-RoF system may be a factor of 40 higher than
that of the D-RoF system having the same optical bandwidth. Conversely, A-RoF
may require a factor of 40 lower optical bandwidth for the same data rate,
where the bandwidth per wavelength is 1 GHz.

To elaborate a little further, Fig. 3 was generated from Eq. (2) by gradually
increasing the bandwidth of each wavelength from 20 MHz to 1 GHz. As shown in
Fig. 3, upon using a bandwidth per wavelength of 1 GHz, we obtain the minimum
sampling rate $f_{s}$ as 2.0357 from Eq. (1). Thus, by setting $f_{s}$
according to Eq. (2), the aggregated D-RoF bandwidth increases to 1954 GHz,
while only 48 GHz is required by A-RoF. Explicitly, their ratio is about 40,
when both transmit at a 384 Gbps throughput, as dictated by
$2W_{bb}N_{t}M\log_{2}(S)$.

Figure 3: Aggregated Bandwidth of A-RoF and D-RoF over Fiber

###  III-B Analysis of the Industrial D-RoF Market

Given the above bandwidth savings of the A-RoF fronthaul over its D-RoF
counterpart, in this section, we review the practical implementation of the
above-mentioned D-RoF system by considering state-of-the-art commercial
products, in order to further demonstrate the potential benefits of A-RoF in
next-generation RANs. As seen in Fig. 2, D-RoF requires high-specification and
costly E/O, WDM-mux, WDM-demux, and PD components capable of processing a high
bandwidth. Hence, when considering the A-RoF bandwidth per wavelength of 1 GHz
(8 Gbps per wavelength) using the configuration of Section III-A, its D-RoF
bandwidth carried by each wavelength relying on digitisation would be 40.71
GHz (81.43 Gbps per wavelength). However, there is a paucity of both
commercial E/O converters supporting a bandwidth of 40.71 GHz, and WDM
Mux/Demux operating at 81.43 Gbps per wavelength. Furthermore, carrying an RF
signal of an aggregated bandwidth of 1954 GHz over a single mode fiber would
be exceptionally challenging due to the severe fiber nonlinearity and fiber
dispersion imposed by a high-bandwidth and high-power system [7].

In addition to its lower bandwidth requirement, an A-RoF based fronthaul
requires less power due to its simplified RRH configuration. Next, we focus on
our attention the power consumption aspects.

##  IV A-RoF versus D-RoF: Power Consumption Analysis

In this section, the power consumption of the A-RoF and the D-RoF system of
Fig. 2 is analysed using the model proposed by Jung et al. [11], where the
source of the total power consumption dissipation is detailed. According to
[11], the power consumption of the CU is imposed by the climate control unit
($P_{CC}$), alternative circuit (AC)/ direct circuit (DC) power supply unit
($P_{ps\\_CU}$), the CU’s signal processing unit ($P_{sp\\_CU}$) including the
MIMO DSP, the baseband modulation, the carrier modulation (and ADC for the
D-RoF) and the E/O convertor ($P_{eo}$). By contrast, the power dissipation of
the RRH is due to the O/E convertor ($P_{oe}$), the AC/DC power supply unit
($P_{ps\\_RRH}$) and the RRH signal processing unit ($P_{sp\\_RRH}$),
including the electronic band-pass filter (EBPF) (and the DAC for the D-RoF).

Note that in the RRH of Fig. 2 and as analysed in [11], where the parameter
values are presented. $P_{sp\\_RRH}$ of the A-RoF and D-RoF would be different
due to the DAC of the D-RoF, resulting in a different signal processing power
consumption. Thus, based on an A-RoF bit rate of 20 Gbps, which would
correspond to about 200 Gbps digital signalling over the fiber for D-RoF, Fig.
4 shows that A-RoF is significantly more energy efficient than its D-RoF
counterpart.

However, there are several challenges that the A-RoF faces, which are
highlighted in the following section, followed by some promising research
directions.

Figure 4: Power Consumption of A-RoF and D-RoF over Fiber Figure 5: EVM of
A-RoF versus D-RoF

Fronthaul Technique |  Bandwidth Efficiency |  Power |  Performance | 5G EVM with 64-QAM | 5G EVM with 256-QAM | Standard
---|---|---|---|---|---|---
A-RoF |  up to 40-fold better than D-RoF |  Power-efficient with massive MIMO |  Poor EVM performance and robustness | Satisfied | not Satisfied | None
D-RoF |  Bandwidth-thirsty |  Power-thirsty with massive MIMO |  High EVM performance and robustness | Satisfied | Satisfied | eCPRI

Table I: A comparison of A-RoF and D-RoF Fronthaul

##  V Status of A-RoF aided RAN

As discussed in Section III, the A-RoF technique is capable of reducing the
bandwidth of D-RoF by a factor of $\frac{f_{s}RC_{w}C}{2W_{bb}}$, which is as
high as 40 upon using 256-QAM for 16$\times$16 MIMO transmission. In this
section, a brief survey is given for reflecting the current status of A-RoF
solutions, followed by the performance comparison of A-RoF over D-RoF using
high-order modulation based high-bandwidth C-RAN systems.

###  V-A Current Status

Given that A-RoF is more susceptible to the fiber nonlinearity than D-RoF, to
the fiber’s chromatic dispersion and to optical noise as well as to the
frequency chirp of directly modulated laser or E/O’s nonlinearity [6], a
comprehensive survey of their mitigation techniques was presented in [12, 6]
by considering the A-RoF’s fiber-link impairments. In this section, we provide
an update on popular techniques of mitigating fiber nonlinearity, the E/O’s
nonlinearity and fiber dispersion.

Fiber-nonlinearity: Fiber-nonlinearity is a power-dependent impairment, which
generates upper harmonics and also non-linear phase rotation. As a result,
when WDM carries multiple wavelengths over a single fiber, the optical power
of each wavelength affects the phase of the optical signals due to the power-
dependent optical Kerr effect [8] caused by the fiber-nonlinearity. For the
traditional single-wave based RAN, the effect of fiber-nonlinearity is
relatively modest due to the low optical power. However, WDM carrying multiple
wavelengths at an increased power makes fiber-nonlinearity the key factor of
influencing the system performance. There are several A-RoF nonlinearity
mitigation techniques [13, 14]. For example, a mmWave A-RoF link relying on
digital predistortion techniques was designed in [13] for mitigating the
fiber-nonlinearity, which achieved a 24 Gbps per wavelength throughput using
16-QAM without employing any digital components in the RRHs [13].
Additionally, a 16-DWDM based A-RoF system having a bandwidth of 20 MHz per
wavelength was proposed for a massive-MIMO system by mitigating the non-linear
stimulated Brillouin scattering [14].

E/O’s Nonlinearity:  The E/O’s nonlinearity is caused by the high amplitude of
its drive signal, which degrades the system performance owing to contamination
by the resultant upper harmonics [6]. For example, the externally modulated
laser inflicts contamination by the upper harmonics onto its output, when its
RF drive signal exhibits high power. Hence, directly modulated laser can be
used for reducing the effect of nonlinearity. A 10-wavelength WDM-based A-RoF
system has successfully transmitted both 16- and 64-QAM symbols over a 20-km
fiber at a bandwidth of 2.5 GHz per wavelength [15]. As a further development,
the optimal RF power and optical power was found in [5] by analysing the
externally modulated laser’s nonlinear curve, which facilitated reliable
transmission by using a bandwidth of 2 GHz per wavelength over 20-km length of
fiber [5]. Thus, the A-RoF system exploiting either the directly modulated
laser or the externally modulated laser is investigated for reducing the E/O’s
nonlinearity.

However, dispersion tends to become more severe in high-bandwidth
communication, hence requiring sophisticated counter-measures [12, 6].

In a nutshell, it may be concluded that similar to D-RoF, WDM-based A-RoF
aided MIMOs also have their pros and cons, but they are capable of meeting
industry’s requirements as discussed below with reference to Table I.

  1. 1.

Performance: In exchange for its wide bandwidth requirement, D-RoF exhibits
superior error vector magnitude (EVM) performance and a high degree of
flexibility without customising the system parameters for various environments
[6, 9].

  2. 2.

Bandwidth and Bit Rate: As mentioned in Section I and Section III, D-RoF is
capable of supporting the 20 Gbps 5G system target, while using off-the-shelf
optical components. This corresponds to an aggregated digitised data rate of
almost 200 Gbps and $<10$ Gbps per wavelength in a 20$\times$20 MIMO system,
which can be readily realised by using commercial optical devices. Its
comparison to A-RoF is shown in Table I.

  3. 3.

Standardisation: The fronthaul market is based on the D-RoF CPRI/eCPRI
solution, but there is no standard for A-ROF system.

Hence, again, in the 5G era D-RoF has better EVM performance than its A-RoF
counterpart, which justifies the industry’s preference for D-RoF. Table I
compares them in terms of their bandwidth efficiency, power efficiency, EVM
performance, 5G feasibility and standardisation status. Explicitly, the
benefits of A-RoF are constituted by its attractive bandwidth and power
efficiency, but its EVM performance has to be improved when using 256-QAM as
discussed in the next section.

Quantitatively, below we consider a 256-QAM system and a baseband bandwidth of
1.87 GHz for the performance comparison of A-RoF and D-RoF in the next section
and highlight some of the challenges of the A-RoF fronthaul.

###  V-B An overview of A-RoF and D-RoF Performance in WDM-based C-RAN
Scenario

In this section, we focus our attention on the performance analysis of A-RoF
versus D-RoF in a high-bandwidth and high-rate WDM-based C-RAN scenario using
the MATLAB-Optisystem. The performance is evaluated using the EVM, a metric
that quantifies the distortion of the received signal constellation for
various number of MIMO antennas. We follow the typical C-RAN architecture of
Fig. 2, where the RF signal is generated off-line using MATLAB. Naturally,
A-RoF conveys analogue signals, while D-RoF transmits a digitised signal after
the ADC. Then, our Optisystem simulates the E/O, WDM Mux/Demux, fiber
transmission and PDs. The recovered RF signals are filtered, amplified and
analysed by MATLAB.

We consider a network transmitting at a rate of 20 Gbps per wavelength, which
is carried by an RF signal of 3 GHz bandwidth, where 256-QAM and a standard
single mode fiber (SSMF) of 15 km are used. The wavelength spacing of the WDM
signal is 200 GHz, while the central wavelength is 1310 nm. To analyse the
impact imposed by the number of antennas on the fiber nonlinearity, Figure 5
shows the EVM performance of both A-RoF and D-RoF upon varying the number of
antennas.

Explicitly, observe in Fig. 5 that the A-RoF system fails to meet the 3GPP EVM
target of 3.5% [1], while the D-RoF system exhibits an EVM of around 3.5%,
which just meets the 3GPP’s requirement for transmitting 256-QAM symbols. By
contrast, the A-RoF shows EVM$\approx$8%, due to the analogue signal’s
susceptibility to fiber nonlinearity. Furthermore, upon increasing the WDM’s
dimension, the dynamic range of the A-RoF system (i.e. the range of the laser
power under which the EVM remains within a certain limits) becomes poorer than
that of D-RoF. Explicitly, it is around 8 dBm for the 4-WDM A-RoF system
compared to 14 dBm for the 4-WDM D-RoF system.

Furthermore, using this 4-WDM system as our example, the right graph of Fig. 5
compares the EVM of the signals transmitted over the channels operating at
different center wavelengths, where D-RoF substantially outperforms A-RoF.

##  VI Future Research of A-RoF

  1. 1.

Mitigating fiber-dispersion and fiber-nonlinearity of A-RoF in high-bandwidth
scenarios:

The employment of optical carrier and side-band suppression techniques as well
as accurately biasing the optical modulators are expected to be helpful in
terms of mitigating the fiber-dispersion [6]. On the other hand, the A-RoF
fronthaul operating at 1310 nm is capable of reducing the fiber’s chromatic
dispersion, hence potentially dispensing with the employment of high-
complexity dispersion compensation techniques in the RRHs. However, the
deleterious nonlinear effect of four-wave mixing may seriously erode the
system’s performance. Thus, a carefully balanced trade-off has to be
maintained between the fiber-dispersion and fiber-nonlinearity.

  2. 2.

Mitigating the non-linear effects in high-order modulation and massive-MIMO
scenarios:

When considering a 256-QAM system, maximising the signal-to-noise ratio (SNR)
is crucial. Increasing the optical power and the RF signal power would improve
the SNR, but the increased optical power increases the impact of fiber-
nonlinearity, which then results in increased E/O conversion nonlinearity.

Therefore, both the E/O converter’s and the fiber’s nonlinearity are affected
both by the RF signal power and by the optical power. Hence, the joint
optimization of both powers is essential for introducing both WDM and 256-QAM
into A-RoF systems, which would have to be followed by verification using
testbed implementations.

Additionally, the optical power of each wavelength of the WDM based massive-
MIMO system may also affect the neighbouring wavelengths as a result of the
fiber’s nonlinearity, where each WDM channel experiences different EVM
performance, as shown in the right graph of Fig. 5. For example, as shown in
the left graph of Fig. 5, the dynamic range of the A-RoF system is degraded
upon increasing the WDM dimension, indicating that the power of the
neighbouring wavelength adversely affects the system performance. Thus,
finding the optimal power levels of all the WDM wavelengths would benefit the
A-RoF’s robustness.

  3. 3.

Silicon Photonics for facilitating the A-RoF deployment:

The key driving force behind silicon photonics is the potential of adopting
the existing Complementary Metal Oxide Semiconductor (CMOS) fabrication
technology for photonic integration in order to create low-cost, yet high-
performance systems. On one hand, the performance of A-RoF is directly
dependent on the fiber-quality. Hence, the employment of silicon photonics may
reduce the nonlinearity or noise of the photonic devices employed. On the
other hand, the objective of both A-RoF and of silicon photonics is that of
reducing the system’s complexity. Since A-RoF requires lower bandwidth than
D-RoF, having an A-RoF based silicon photonics architecture meeting the
current capacity may be deemed realistic.

Although there are critical challenges ahead, promoting the standardisation of
A-RoF based RANs may inspire the industry to fabricate the required analogue
components. Specific use-cases, such as small-cell cellular networks may be
considered for the initial employment of A-RoF, since they require high-rate
data transmission and small RRHs, where A-RoF has the edge in terms of its
bandwidth- and energy-efficiency over D-RoF.

##  VII Conclusions

The operational D-RoF based RAN fronthaul requires an excessive bandwidth over
the fiber-link. Hence, we have critically appraised the potential of A-RoF
solutions in circumventing this impediment in the context of massive MIMO
aided high-order QAM schemes. We have compared their bandwidth- and power-
efficiency, demonstrating that A-RoF may be capable of reducing the bandwidth
required by a factor of 40, and hence it is capable of relaxing the hardware
specifications, while significantly reducing the power-consumption. However,
A-RoF requires substantial further research in order to guarantee the bit rate
requirements of next-generation applications.

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|  Yichuan<EMAIL_ADDRESS>is an Assistant Professor with Harbin
Institute of Technology (Shenzhen). He received his B.Sc. degree in Optics
Information Science and Technology from China University of Petroleum (East
China), Qingdao, China, in 2012, and earned his M.Sc. degree and Ph.D. degree
in wireless communications from the University of Southampton, Southampton,
UK., in 2014 and 2019. He was a research assistant in the Lightwave
Communication Lab of the Chinese University of Hong Kong (CUHK) from July to
October in 2017. His research is focused on the the radio over fiber for
backhaul, fronthaul and indoor communication network. His research interests
are millimeter wave over fiber, optical fiber aided analogue beamforming
techniques, Multi-functional MIMO, mode division multiplexing in multimode
fiber and fiber-based C-RAN system.
---|---
|  Qijie Xie<EMAIL_ADDRESS>received his Ph.D. degree in Electronic
Engineering from The Chinese University of Hong Kong (CUHK) in 2018. Until
June 2020, he had been a postdoctoral research associate in Prof. Chester
Shu’s group, CUHK. Now, he is working as an assistant researcher in Pengcheng
Laboratory, Shenzhen, China.
---|---
|  Mohammed<EMAIL_ADDRESS>is an Associate Professor in the
Department of Electronics and Computer Science in the University of
Southampton. He received his PhD in Wireless Communications from the
University of Southampton, UK in 2008. Following the PhD, he joined
Imagination Technologies as a design engineer, where he worked on designing
and developing Imagination’s multi-standard communications platform, which
resulted in several patents. He is the recipient of several academic awards
and has published a Wiley-IEEE book and in excess of 80 journal and conference
papers. Mohammed’s research interests include the development of intelligent
communications systems, energy-efficient transceiver design, MIMO, millimeter
wave communications and Radio over fiber network design.
---|---
|  Lajos<EMAIL_ADDRESS>(http://www-mobile.ecs.soton.ac.uk)
(FIEEE’04, Fellow of the Royal Academy of Engineering F(REng), of the IET and
of EURASIP), received his Master degree and Doctorate in 1976 and 1983,
respectively from the Technical University (TU) of Budapest. He was also
awarded the Doctor of Sciences (DSc) degree by the University of Southampton
(2004) and Honorary Doctorates by the TU of Budapest (2009) and by the
University of Edinburgh (2015). He is a Foreign Member of the Hungarian
Academy of Sciences and a former Editor-in-Chief of the IEEE Press. He has
served several terms as Governor of both IEEE ComSoc and of VTS. He has
published 1900+ contributions at IEEE Xplore, 19 Wiley-IEEE Press books and
has helped the fast-track career of 123 PhD students. Over 40 of them are
Professors at various stages of their careers in academia and many of them are
leading scientists in the wireless industry.
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