# 5G Advanced: Wireless Channel Virtualization and Resource Mapping for Real
Time Spectrum Sharing

Walaa Alqwider, Aly Sabri Abdalla and Vuk Marojevic
Department of Electrical and Computer Engineering, Mississippi State
University, MS, USA
Emails: {wq27, asa298, and<EMAIL_ADDRESS>
###### Abstract

The coexistence between active wireless communications and passive RF spectrum
use becomes an increasingly important requirement for coordinated spectrum
access supporting critical services. The ongoing research and technological
progress are focused on effective spectrum utilization including large-scale
MIMO and energy efficient and low-power communications, innovative spectrum
use and management, and resilient spectrum sharing, just to name a few. This
paper introduces a new tool for real time spectrum sharing among emerging
cellular networks and passive RF sensing systems used for remote sensing and
radio astronomy, among others. Specifically we propose leveraging wireless
channel virtualization and propose a virtual-to-physical resource mapping
framework, mapping types, and control signaling that extends the current 5G
New Radio (NR) specifications. Our technology introduces minimal changes to
the protocol and is meant to be transparent to the end user application. We
validate the proposed technology by extending a 3GPP compliant 5G NR downlink
simulator and identify further research directions where work is needed on
designing effective ways to explicitly signal the need for spectrum or
spectrum use predictions.

###### Index Terms:

5G NR, 5G Advanced, wireless, virtualization, real time, spectrum sharing, RF
sensing.

##  I Introduction

Radio access and management are defining 5G wireless networks which are
expected to support increasingly diverse and challenging service demands.
Society depends heavily on wireless technologies for commerce, transportation,
health, science, and defense, with emerging technologies such as the Internet
of Things (IoT) and unmanned aerial vehicles (UAVs) further increasing this
dependence. Radio astronomy (RA), remote sensing (RS), and other passive RF
sensing services are also indispensable in modern society. One important RS
application in Earth science and climate studies is to monitor the soil
moisture, which provides scientists with critical information for effective
agricultural decisions, as well as for forecasting severe weather, floods, and
droughts. These sensors measure extremely low-power natural RF signals and
cannot tolerate radio frequency interference (RFI).

The growth of active wireless systems often increases RFI experienced by
passive systems. Such RFI may introduce bias in the passive system’s
measurements or render the passive system completely useless. While additional
spectrum is being considered and techniques for higher spectrum efficiencies
are being developed, better spectrum reuse and sharing technologies are
becoming more important than ever to enable scaling wireless communications
services while protecting passive RF systems. The spectrum coexistence between
wireless communications and sensing is therefore an increasingly important
area of research and development (R&D).

Radio frames of wireless protocols have a certain structure, which is more or
less flexible. A 4G long-term evolution (LTE) base station is always on and
transmits control channels and signals even when no users are being served.
That is, the downlink frame will be sparsely populated when there are few or
no users, but will use up to 25% of the resource elements in each subframe
with several control channels and signals that will appear to nearby RF
sensing systems operating in the same band as strong RFI. 5G new radio (NR)
allows a more flexible configuration of channels and signals than LTE. A 5G
base station can schedule control channels more dynamically along with user
data and be put to temporary sleep [1]. Passive RF systems, such as RA or RS,
often do not have the ability to process and mitigate RFI. The problem
therefore consists in modifying radio access networks for enabling coexistence
with passive RF systems. Minimal changes to software processes as opposed to
hardware redesign would enable rapid deployment as well as backward
compatibility with legacy wireless networks.

Figure 1: Spectrum coexistence enabled by wireless channel virtualization and
virtual-to-physical resource mapping.

This paper considers active wireless communications systems, specifically
modern cellular networks, and explores a path for their evolution to enable
harmonious coexistence with passive RF systems. Cellular transmissions follow
a well-organized and by and large regular frame structure. This is very
efficient for operation in licensed spectrum. We leverage the flexibility of
5G NR and introduce the wireless channel virtualization and a virtual-to-
physical resource mapping framework to enable fine-grain transmission control
for managing the RFI to passive RF systems. The proposed virtual-to-physical
resource mapping design provides an additional layer of radio resource
abstraction and extends the 5G NR physical layer processing chain to enable
real-time spectrum sharing. Fig. 1 illustrates this concept.

The current techniques for sharing the spectrum among active users, mainly
communications users, cannot be directly applied to spectrum sharing between
active and passive users. However, there have been recent studies that focus
on enabling spectrum sharing between active and passive RF systems [2, 3, 4].
The virtualization concept has became popular in wireless networking research
to enable flexible deployment and access [5, 6, 7] and is a feature of the 5G
core network [8, 9]. Reference [5] proposes a framework for wireless network
virtualization consisting of three planes: the data plane, the cognitive
plane, and the control plane. A hierarchical control scheme based on cell
clustering is designed to coordinate the virtualized networks. Most of the
prior works on wireless network virtualization are related to network slicing
which virtualizes the network for multiple operators to access different
slices of a common infrastructure/resource [10, 11].

The rest of the paper is organized as follows: : Section II presents the
current 3GPP specifications for supporting the virtual-to-physical mapping.
Section III introduces the proposed virtual-to-physical mapping framework.
Section IV shows an example use case of the proposed virtual-to-physical
mapping for 5G network with different mapping types. Section V demonstrates
the potential research directions that should be studied in the future work.
Finally, Section VI concludes the paper with the most appealing findings.

##  II 5G NR Virtual-to-Physical Resource Mapping Background

###  II-A Mapping

The virtual resources block (VRB) was introduced in 3GPP Release 15 for 5G NR.
It contains the modulation symbols that are mapped to a physical resource
block (PRB) within the active bandwidth part (BWP) as part of the physical
layer processing at the gNodeB. The VRB-to-PRB mapping was introduced in 5G NR
to add more flexibility for radio resources allocation while decreasing the
control signaling overhead. It is meant for offering frequency diversity to
handle the variations in the channel quality especially for high mobility
devices [12].

The 3GPP specifications support two virtual-to-physical resource mapping
types: the non-interleaved and interleaved mapping. The former maps the VRBs
in the BWP directly to the same PRB in the BWP. The latter uses an interleaver
that takes the contiguous VRBs and distributes them across the BWP. These two
mapping options are illustrated in Figs. 2a and b.

(a) 5G NR non-interleaved resource mapping

(b) 5G NR interleaved resource mapping

(c) Proposed 5G Advanced virtual-to -physical resource mapping

Figure 2: Virtual-to-physical resource mapping in 5G NR (a, b) and for 5G
Advanced (c).

###  II-B Control Signaling

The 3GPP specifications support two types of downlink resource allocation in
the frequency domain. For both types, the gNodeB assigns VRBs to UEs, but the
continuity of the allocated RBs and the number of bits used to inform the UEs
about the allocation differ between the two types. Type 0 groups the available
RBs in the BWP in virtual resources block groups (VRBGs). One or more
contiguous or non-contiguous VRBGs within the BWP can be allocated to a UE.
For example, VRBGs 1 and 5 may be allocated to a UE. Resources allocation Type
1, on the other hand, assigns contiguous VRBs to each UE [13].

The gNodeB informs the UEs about the set of RBs and orthogonal frequency
division multiplexing (OFDM) symbols allocated for data transmission by
encoding the scheduling information in the downlink control channel (DCI)
which is carried by the Physical Downlink Control Channel (PDCCH) [14]. Once
the UE detects a valid PDCCH it extract the DCI and decodes the scheduling
information to find out where to expect its downlink data or where it can send
data on the uplink.

The different types of DCI formats are illustrated in Table I [14]. Resource
allocation Type 0 employs bit mapping. That is, the number of bits that are
needed to inform the UE about the allocated VRBGs is equal to the number of
VRBGs within the BWP. For Type 1, the gNodeB encodes the starting virtual
resource block ($RB_{start}$) and the length of the contiguously allocated RBs
($L_{RBs}$) as a resource indication value (RIV) in the resource allocation
field of the DCI. This requires fewer bits than for Type 0. Type 0 thus
provides more flexibility than Type 1, but it needs more control bits to
inform the UEs about the RB allocation.

Non-interleaved mapping can be used for both allocation types, Type 0 and Type
1, whereas the interleaved mapping is used only with Type 1. The reason behind
not using the interleaved mapping with Type 0 is that Type 0 can distribute
the RBs allocated to a signle UE across the BWP for frequency diversity. Type
1 provides low-overhead control signaling where the interleaved mapping
achieves the frequency diversity.

Non-interleaved mapping is recommended with Type 1 signaling and a channel-
dependent scheduler which assigns contiguous VRBs with the best channel
conditions to the selected devices. Employing the interleaved mapping in this
case would result in using RBs with worse channel conditions. Interleaved
mapping is recommended with Type 1 in the case where scheduling decisions are
not driven by the channel conditions or in high-mobility scenarios which make
it difficult to follow the rapid channel variations. This scheme can also help
with quality averaging across the transport blocks in the case where the
resource allocation for one UE spans the entire BWP experiencing channel
quality variations.

TABLE I:  5G NR DCI formats. DCI format |  Usage
---|---
DCI 0_0/0_1/0_2 |  Schedule the Physical Uplink Shared Channel (PUSCH) in the cell
DCI 1_0/1_1/1_2 |  Schedule the Physical Downlink Shared Channel (PDSCH) in the cell
DCI 2_0 |  Notify a group of UEs of the slot format
DCI 2_1 |  Notify a group of UEs of the PRB(s) and OFDM symbol(s) where no transmission is intended for them
DCI 2_2 |  Provide transmit power control (TPC) commands for the Physical Uplink Control Channel (PUCCH) and PUSCH
DCI 2_3 |  Transmit a group of TPC commands for sounding reference signal (SRS) transmission by one or more UEs
DCI 2_4 |  Notify a group of UEs of the PRB(s) and OFDM symbol(s) where to cancel the corresponding uplink transmissions
DCI 2_5 |  Notify UEs about the availability of soft resources
DCI 2_6 |  Notify one or more UEs about the power saving information outside the discontinuous reception active time
DCI 3_0 |  Schedule the NR sidelink in the cell
DCI 3_1 |  Schedule the LTE sidelink in the cell

##  III Proposed Virtual-to-Physical Resource Mapping Framework

We propose wireless channel virtualization to control the wireless spectrum
access for real-time spectrum sharing. Our virtual-to-physical resources
mapping framework operates in both the frequency and time domains with the
goal of avoiding active co-channel transmission during RF sensing periods.
Fig. 3 illustrates this. This technology is agnostic to the wireless system
and mobile network generation.

Figure 3: Active and passive RF coexistence: RFI experienced by the RF
sensor(a) and RFI avoidance (b).

Let us assume an xG network that provides regular services and continuously
feeds data to its users over the wireless link. An xG transmitter packages the
data and performs symbol and bit-level processing at the physical layer, using
traditional digital signal processing or emerging data-driven techniques.
Instead of directly generating the physical channels, that is, creating
protocol-specific over-the-air transmission patterns, the processed transport
channels become the virtual channels that use virtual spectrum and virtual
radio resources instead. In licensed spectrum, these virtual channels may map
directly to the xG physical control and data channels as per the standard and
the scheduler. In shared spectrum, these channels may be shifted in time or
frequency, or both. Fig. 2c illustrates this type of mapping in comparison
with the 5G NR mapping options of Fig. 2a and b. In this illustrative example,
a bandwidth and time segments are avoided by spreading the four collocated
VRBs in frequency and time. The blue block may represent time sensitive data
and is therefore transmitted without delay.

Leveraging the fact that many passive radio services do not sense the same
band continuously, we propose sharing this band with xG networks that
implement the virtual-to-physical resources mapping for enabling flexible, on-
demand transmissions, as well as silence patters to avoid harmful RFI. The
virtual channels allocated to the xG system users will be mapped to physical
channels opportunistically or systematically to minimize the RFI that the xG
network may cause to other spectrum consumers and natural RF sensing systems,
in particular. For example, the virtual channels can be grouped for uplink or
downlink transmission bursts over the air, enabling silent periods, spread in
both time and frequency for creating sparse OFDM grids, or partially moved to
different frequencies, e.g. licensed bands, when the spectrum is needed for
natural RF data collection.

Fig. 4 shows the flow of physical layer processing in 5G NR with the proposed
mapping framework. The base station scheduler, which is part of the medium
access control layer, assigns VRBs to UEs. It may schedule VRBs to users in
each transmission time interval (TTI) as a function of different inputs, such
as channel quality indicators, buffer statuses, service dependent performance
requirements, or other scheduling criteria. The data and control information
pass through physical layer processes such as coding and modulation and the
resource mapping combines the different streams from the physical channels
according to the standard but maps them to the virtual resource grid, that is
to virtual time-frequency resources and not the actual physical resource. The
virtual-to-physical resources mapping block is the interface or buffer between
the virtual and the physical resources. It can change the physical appearance
of the virtual channels and create different transmission patters. This block
is followed by further processing in the digital and analog domains and the
physical RF transmission.

As indicated in Fig. 4 the specific mapping may be determined by different
factors. For example, the base station may receive a signal notifying about an
RF sensing period in that area. If no sensing is expected to occur in the
upcoming TTI, the direct mapping can be used. If there is sensing activity,
the base station will consider the service that the scheduled data supports
and if it is time sensitive and cannot be delayed, these VRBs will be mapped
to a band that does not affect the passive receiver. If the service is not
time sensitive, the base station may decide to delay the transmission. If the
sensing period is long, the channel quality will likely change and the
virtual-to-physical resource mapping may relocate the VRBs in the physical
resource grid by means of interleaving, for example.

Figure 4: 5G Advanced physical layer building blocks with the proposed
virtual-to-physical resource mapping.

While the proposed mechanism may incur processing overhead and latency, it
enhances the spectrum utilization by leveraging bands or areas that are
currently prohibited for communications, because they are used by highly-
sensitive passive systems that cannot tolerate RFI. Using virtualization
provides the flexibility to adapt the physical transmission for each
transmitter, band, and service, and allows considering the quality of the RF
hardware, RF impairments, and out-of band emissions, to dynamically control
co-channel and adjacent channel interference.

##  IV Virtual-to-Physical Mapping Technology

This section presents the virtual-to-physical mapping technology and
signaling. These are integrated into MATLAB’s 5G Toolbox. We consider resource
allocation Type 1 for the VRB allocation where the gNodeB scheduler allocates
contiguous VRBs to users in each TTI for downlink transmission. BWPs consist
of 50 RBs and three UEs are scheduled in time slots 1, 2 and 3 as illustrated
in Fig. 5a. We present four fundamental mapping types.

###  IV-A Virtual-to-Physical Mapping Types

####  IV-A1 Direct Mapping

The direct mapping allocates PRBs that match the VRBs without any shift in the
time or frequency domain. This is shown in Fig. 5b. and used when the spectrum
is available to the 5G system. The 5G users will receive their PDSCH based on
the MAC layer assignments without any changes at the physical layer.

####  IV-A2 Time Shifted Mapping

The second type of mapping buffers the data for later transmission. The length
of the buffer can be defined in time slots and depends on the passive system
needs. The allocated VRBs are mapped to PRBs with a time delay while
maintaining the same structure as shown in Fig. 5c. This mapping uses the same
spectrum as originally scheduled, but introduces a delay that certain services
can tolerate but other may not. For latency sensitive applications other
mapping types may need to be considered.

####  IV-A3 Time and Frequency Shifted Mapping

When buffering the data for transmission in later time slots as per the
mapping type 2, channel-dependent scheduling schemes may become ineffective.
Therefore, we introduce the time and frequency shifted mapping (Fig. 5d). It
allows delaying transmission and changing the frequency allocation for the UEs
for frequency diversity. If CQI is available, these can be used for the
reallocation. Alternatively, historical data may be available to predict the
CQI for each user. Large shifts in time as determined by the mapping may
trigger the rescheduling of VRBs.

####  IV-A4 Shift to another BWP

When delaying the transmission is not suitable, for example, for time
sensitive data, a frequency shifted mapping is proposed and is illustrated in
Fig. 5e. We assume the case of the 5G NR network, the allocated frequency is
divided in BWPs. Therefore, to free the current BWP for the passive device,
the mapping block shifts the allocated VRBs to another available BWP and
informs the users about this frequency shift by sending a signal to activate
the new BWP. The advantage of this mapping is that no latency is incurred
which is needed when latency is the critical metric. The new BWP may within
the carrier licensed spectrum and the original BWP may be in the shared
spectrum that cannot be momentarily used for communications.

(a) VRB allocation (b) Type 1: Direct mapping (c) Type 2: Time shifted mapping

(d) Type 3: Time and frequency shifted mapping  (e) Type 4: BWP shifted
mapping

Figure 5: Examples of different types of mapping for 5G NR waveform

###  IV-B Control Signaling and Processing

The proposed virtual-to-physical resource mapping, or VRB-to-PRB mapping,
needs special control signals to inform the active users about the mapping so
that these users can look for there downlink data in the correct place in the
frequency and time domains. This applies for the uplink as well, but it may be
the base station that controls it. For the sake of brevity, we discuss the
downlink here.

The base station needs to send a control signal to inform the user which
mapping type it uses. As we have 4 different types we can use a control signal
with two bits (00, 01, 10, 11) to indicate direct mapping, time shifted
mapping, time and frequency shifted mapping, and BWP shifted mapping,
respectively. No additional more control signal is needed for the first
mapping type because the active users will receive the downlink data in the
same frequency and time resources as signaled in the PDCCH informing the users
about their scheduled resources. For the mapping type 2, the active users need
to know the delay of the downlink data. The base station thus needs to signal
the time shift. For mapping type 3, which is the most complex type, the new
frequency and time allocations need to be signaled. For mapping type 4, if an
active user uses more than 2 BWPs it needs to know in which BWP it should
expect the data and, therefore, additional control signaling is needed for
this type to indicate the BWP. Such signaling needs to happen as soon as the
mapping is determined to inform the receivers for coherent demodulation and
for potential power saving.

Section II introduced the DCI formats that are used in 5G NR to carry downlink
scheduling assignments, uplink scheduling grants, and other control
information for one user or a group of users. The scheduling information is
used to inform a UE which RBs in the frequency domain and where in the time
domain to expect downlink data on the PDSCH or where to send uplink data on
the PUSCH. The DCI data is attached with a 24-bit cyclic redundancy check
(CRC) which is scrambled with the corresponding identifier referred to as a
radio network temporary identifier (RNTI). Using the RNTI mask, the UE can
detect the DCI for its unicast data and distinguish sets of DCIs with
different purposes that have the same payload size. The UE might receive the
DCI information in the same slot where it intends to receive the data. This is
called self-contained slot because it contains both the scheduling information
and the data.

We propose leveraging the existing DCI formats to signal about the changes in
the physical resources allocation. For the first mapping type, no additional
signaling is needed since the UE received the data as determined by the
scheduler and already encapsulated in the DCI as per the 3GPP standards. For
all other mapping types introduced above we can make use of the self-contained
slot principle. The gNodeB can use DCI 2_1 to notify the group of UEs that (a)
the allocated resources are transmitted on another BWP in case of mapping type
4, or (b) indicate the number of slots that the UEs will not receive any data
on for mapping types 2 and 3. Informing the UEs about the empty slots where no
data is transmitted will help improve the energy consumption as the UEs can go
to idle state during these time slots. The DCI 2_1 format can be modified to
inform the UEs about the type of mapping used and any other essential
information associated with this mapping type, such as the new BWP in case of
mapping type 4, the number of shifted time slots in case of mapping types 2
and 3. For mapping type 3 where the PRBs will not match the VRBs shifted in
time, the gNodeB can encode the changes in PRBs using DCI 1_0/1_1 for the case
of downlink transmission.

##  V Research Directions

The proposed wireless channel virtualization and virtual-to-physical resource
mapping provides a new tool for real time spectrum sharing. More research,
prototyping, and testing is needed. We recommend the following research
directions.

  * •

Passive system feedback mechanisms: Generally there are different options for
informing about sensing periods. The data can be available beforehand and
stored in a database that is accessible to the active transmission system.
Oftentimes, this is not feasible because of privacy and security concerns. For
real time spectrum sharing, we propose other types of control signaling for
coordinating active and passive use of spectrum. Different types of sensors
may offer different solutions as a function of location and sensing patterns.
RF sensors such as the SMAP satellites currently provide global RFI
information where RFI was detected in the past. Research needs to study active
feedback mechanisms or predictions before RFI happens.

  * •

Coordinated virtual-to-physical resources mapping: We suggest research on data
driven learning approaches to create more dynamic, efficient, and coordinated
mapping between the virtual to physical resources. There are a limited number
of major passive sensing systems at static and well-known locations and their
frequencies and sensing patterns may be known or predicted. It may also be
possible to evaluate RFI from collected and processed data at the sensing
sites to create and maintain interference maps. Such maps can be dynamically
updated and help finding the optimal mapping opportunities between the virtual
and physical resources. When done proactively, e.g. through tighter
collaboration with passive RF systems and leveraging advanced computing and
communications principles, such as caching or prefetching of data, real-time
performance can be improved for active communications while minimizing RFI to
passive users.

  * •

Cross-layer coordination: The link and system performance when applying
virtual-to-physical resource mapping needs to be evaluated in different radio
environments. Specifically, for frequency selective channels and where the
time shift is larger than the channel coherence time, schedulers may need to
be redesigned or scheduling decisions reevaluated. Similarly, precoding for
MIMO and beamforming may be suboptimal if CSI is outdated. These and other
channel-driven processes therefore have to be evaluated for the introduced
frequency and time agility of the proposed transceiver.

  * •

Frequency-selective waveform design: It is possible to design waveforms that
embed certain features, e.g. cyclostationary signal features, that allow
message decoding even after removing parts of the signal before transmission
that would otherwise cause strong RFI. This approach is challenging as it
needs careful consideration of transceiver characteristics, including
processing complexity and peak-to-average Power ratio characteristics. The
design of such waveforms need to consider the characteristics of the passive
system, its observations, and the equipment used. This can be explored an an
alternative technology to the one proposed in this paper or achieved through a
custom virtual-to-physical mapping block.

  * •

Machine learning-enhanced mapping: Machine learning (ML) and, specifically,
reinforcement learning (RL) should be explored for making a cleaver mapping
decisions based on predictions. For example, ML can predict the channel
quality for the different users in different PRBs for the upcoming time slots
by providing a series of the past CQIs for the different users to estimate the
expected CQIs for the next time slots. The output of the ML CQI predictor will
be fed to the RL algorithm in order in to decide which VRBs to mapped to which
PRBs to optimize the use of resources for communication when they are
available. This method can run while the passive receiver is using the
spectrum.

##  VI Conclusions

This paper has introduced wireless channel virtualization and a virtual-to-
physical resource mapping framework for the spectrum coexistence between
active and passive RF systems. Specifically, we consider 5G and extend its
current protocol features to enable real time spectrum sharing. We introduce a
layer of abstraction at the physical layer to dynamically control access to
physical radio resource in time and frequency. We introduce several mapping
types and control signaling that leverage and extend the current 5G NR
specifications. Our technology introduces minimal changes to the protocol and
is meant to be transparent to the end user application. We validate the
proposed technology by extending a 3GPP compliant 5G NR downlink simulator and
identify further research directions where work is needed on designing
effective ways to explicitly signal the need for spectrum or spectrum use
predictions.

## Acknowledgment

This work was supported in part by NSF grant ECCS-2030291.

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