#  A Cooperative NOMA User Pairing in UAV-Based Wireless Networks

Ghania Khraimech, Fatiha Merazka  LISIC Lab. Telecommunications Department,
Electrical Engineering Faculty,
USTHB University,
16111, Algiers, Algeria
<EMAIL_ADDRESS>
###### Abstract

NOMA (non-orthogonal multiple access) will be seen as a promising technology
for enhancing spectrum efficiency in future cellular networks. The use of
unmanned aerial vehicle (UAV) Amplify-and-Forward (AF) relaying as a moving
access point or BS, on the other hand, has emerged as a potential solution to
wireless networks’ high traffic demands. In this paper, we study joint user
pair and resource allocation-based distance to optimize fair throughput in a
downlink scenario, which concentrates on UAV-aided communication from
different wireless-powered nodes. Several transmission methods are proposed,
including NOMA as well as UAV cooperative relaying and two representative
node-pairing strategies. The simulation results illustrate that the proposed
user pairing strategies for cooperative NOMA and UAV-based cellular networks
enhance downlink transmission performance and ensure optimum use of power and
bandwidth resources.

###### Index Terms:

NOMA, UAV, User pairing, Cooperative communication, Amplify-and-forward

##  I Introduction

Due to considerable technological developments across several drone-related
domains ranging from embedded systems to autonomy, control, security, and
communications, the domain of unmanned aerial vehicles (UAVs), sometimes known
as drones, has undergone a significant revolution in recent years. UAV
communications are also a useful strategy to provide communication links
throughout temporary events and after catastrophes in distant regions missing
cellular infrastructure [1]-[5]. One of the encouraging use case scenarios of
UAVs is designing flexible, adaptable, and wireless multiple antennas in the
sky [6]. Because of the high elevation of UAVs, line-of-sight (LoS) in UAV
communications can provide better small-scale fading between UAVs and ground
customers than in traditional ground links, which presents both opportunities,
as well as challenges in the development of UAV cellular networks citeb7. Due
to the obvious low - power consumption of UAVs, achieving enhanced spectrum
and energy efficiency is essential to the ability to get the most out of UAV-
based network technologies [8]-[10].

NOMA seems to be a useful product for achieving both spectrum efficiency and
energy efficiency in next-generation wireless technologies and beyond,
especially in UAV communication networks [11]. The potential advantage of NOMA
over OMA in a cellular communication system where a base station (BS) is
equipped with enormous antenna arrays was successfully investigated in [12].
In comparison to conventional OMA techniques, NOMA is capable of a more
efficient manner utilizing available resources by arbitrarily maximizing
users’ specific fading channel on both single-cell and cellular systems
[13],[14], and it is ready to accommodate multiple members with specific
quality-of-service (QoS) criteria in the same users access [15]–[17].
Theoretically, spectral efficiency combining within a frequency, time, and
code block delivers the composite signal to multiple customers concurrently in
NOMA. NOMA is based on the use of superposition coding (SC) at the transmitter
and successive interference cancellation (SIC) techniques at the receiver [4],
[12]. Multiple access users in the power domain can be implemented in this
scenario by using various energy degrees for consumers in the same resource
block. As a result, by employing NOMA techniques to increase the available
spectrum and energy efficiency, UAV networks may offer several users at the
same time [18].

The downlink of a UAV-enabled wireless communication is investigated in this
article using power-domain NOMA and cooperative relaying, with the ground
nodes being wireless-powered equipment. Air-to-ground (A2G) communication
channels[19] are used by these devices, and they are characterized by an
altitude-dependent route loss exponent and fading. We focus on a user pairing
system for NOMA with cooperative relaying cellular networks, in which access
devices are separated into two groups and a unit pair is produced from each
group. After that, the channel capacity is divided by the number of pairs,
with each pair sharing the same sub-channel to deliver their data. The primary
issues that we address in this work are related to how to pair NOMA users
within each available orthogonal resource, user-pairing strategy for NOMA as
well as cooperative relaying in our UAV model, which has a better performance
given a specific user pairing strategy.

The remainder of this paper is organized as follows. In Section II, we present
the system and channel models considered throughout this article. Section III
details user pairing in cooperative NOMA. Performance analysis of the
Cooperative NOMA-based transmission and pairing strategies evaluated in this
work are described in Section IV. The simulation results are provided and
discussed in Section V. Finally, Section VI presents general conclusions
regarding the obtained results in this paper as well as prospects for our
future work.

##  II System Model

As shown in Fig. 1, we consider a link between a transmitter BS and receivers,
4 users, separated by a distance $R_{i}$. We assume that direct communication
between the transmitter and the receivers is possible, where there is one
source node BS, $K$ half-duplex amplify-and-forward (AF) UAV relay nodes
denoted as $R_{k},k=1,...,K$ and four users $U_{1},U_{2},U_{3},$ and $U_{4}$,
respectively. We use UAVs, each equipped with a single antenna, to enable this
link and maximize its capacity. Each UAV acts as an amplify-and-forward relay
where it simply receives the signal, amplifies, and re-transmits it in a
synchronized manner.

$[H]_{1}\in C\textsuperscript{$N_{T}$×$N_{R}$}$ denotes the communication
between the transmitter and the UAVs, whereas, $[H]_{2}\in
C\textsuperscript{$N_{R}$×$N_{U}$}$ denotes the channel between the UAVs and
the receiver. We assume that both channels are strong LoS channels with each
element defined as $[H]_{i,j}=\frac{\lambda}{4\pi d_{i,j}}e^{\frac{j2\pi
d_{i,j}}{\lambda}}$, where $\lambda$ where are the signal frequency band and
$d_{i,j}$ is the distance between antenna elements $i$ and $j$ at the
transmitter, relay or receiver.

Figure 1: Illustration of the system model.

##  III User Pairing in Cooperative NOMA

###  III-A Cooperative NOMA

We know that NOMA exploits successive interference cancellation (SIC) [20], in
which one user detects the other user’s message from the superposition-coded
incoming signal before decoding his own. Thus, while conducting SIC, the near
user decodes the information of the far user. This is a step that cannot be
ignored. In every case, the data of the far user must be decoded by the near
user.

Now that the near user has access to the far user’s data, they can help him by
relaying it to him. Because the distant user’s channel with the broadcasting
BS is poor, the close user’s retransmission of his data will supply him with
diversity. In other words, he’ll get two copies of the same message. One comes
from the BS, and the other comes from a nearby user who is serving as a relay.
As a result, we can assume the far user’s outage probability to reduce.

Cooperative communication/cooperative relaying [21] is the name for this
concept. We can see that NOMA naturally encourages cooperative communication
because the near user has access to the data of the far user, which they must
decode anyway. The benefit of cooperative communication is link redundancy,
which allows a message to be transmitted twice. In those other words, if one
link is down, the other is very certainly up and running. The scenario in
which both links fall at the same time is less likely than one link going down
at a time. We have a lower outage probability and, as a result, more diversity
benefits without the need for extra antennas in this situation (i.e., MIMO).
Another benefit of relaying is that it might essentially extend the BS’s
coverage area. Let’s create a cooperative NOMA network now that we’ve seen
what cooperative communication is and how it might benefit our network. Under
a line-of-sight channel, we’ll consider a downlink transmission with a BS and
two NOMA users relayed by a UAV acting as an amplify-and-forward relay.[22]
With the UAV, we have a near user with a stronger channel and a distance user
with weak channel circumstances. The transmission is split into two parts. Let
us refer to the first time slot as ”direct transmission” and the second as
”relaying.”

####  III-A1 Direct transmission slot

In the direct transmission slot, the BS uses NOMA to transmit data intended
for the near user $x_{n}$ and the far user $x_{f}$. The near user uses SIC to
decode the far user’s data first and then proceeds to decode its data. The far
user will only perform direct decoding.

At the end of the direct transmission slot, the achievable data rates at the
near user and far user are,

$R_{n}=\frac{1}{2}log_{2}(1+\alpha_{n}\rho\lvert h_{n}\rvert^{2}).$ (1)

for the detection of SIC, the desired signal and interference signal is
presented as

$R_{f,n}=\frac{1}{2}log_{2}(1+\frac{\alpha_{f}\rho\lvert
h_{f}\rvert^{2}}{{\alpha_{n}}\rho\lvert h_{f}\rvert^{2}+1}).$ (2)

Notation

  * •

$\alpha_{n}$: power allocation coefficient for the near user.

  * •

$\alpha_{f}$: power allocation coefficient for the far user.

  * •

$h_{n}$ : the channel between BS and near the user.

  * •

$h_{f}$ : the channel between BS and far user.

  * •

$\rho$: transmit $SNR=\frac{P}{\sigma^{2}}$, where $P$ is the transmit power
and $\sigma^{2}$ is the noise variance

  * •

As usual, $\alpha_{f}>\alpha_{n}$, and, $\alpha_{n}$+$\alpha_{f}$=1

We have this factor of $\frac{1}{2}$ in front of the achievable rates because
we have two time slots of equal duration and $R_{n}$ , $R_{f}$ are the
achievable rates during the first time slot alone.

####  III-A2 Relaying slot

The next half of the time slot is called the relaying slot. As we saw, the
near user already has the far user’s data because he decoded it in the
previous time slot. In the relaying time slot, the near user just transmits
this data to the far user. The achievable rate of the far user at the end of
the relaying slot is,

$\mathbf{R}_{f,2}=\frac{1}{2}log_{2}(1+\rho\lvert\mathbf{h}_{nf}\lvert^{2})$
(3)

here, $h_{nf}$ is the channel between the near user and the far user. We can
already see that $R_{f,2}>R_{f,1}$ because of two reasons:

  * •

There is no interference from other transmissions

  * •

There is no fractional power allocation. The whole transmit power is given to
the far user

####  III-A3 Diversity combining

Now, at the end of the two-time slots, the far user has two copies of the same
information received through two different channels. The far user can now use
a diversity combining technique. For example, he can use selection combining
to choose the copy which was received with high SNR. After selection
combining, the achievable rate of the far user would be,

$\mathbf{R}_{f}=\frac{1}{2}log_{2}(1+\max(\frac{\alpha_{f}\rho\lvert\mathbf{h}_{f}\rvert^{2}}{{\alpha_{n}}\rho\lvert\mathbf{h}_{f}\rvert^{2}+1},\rho\lvert\mathbf{h}_{f}\rvert^{2}))$
(4)

If we do not use cooperative relaying, the achievable rate of the far user
would be,

$\mathbf{R}_{f,noncoop}=log_{2}(1+\frac{\alpha_{f}\rho\lvert\mathbf{h}_{f}\rvert^{2}}{{\alpha_{n}}\rho\lvert\mathbf{h}_{f}\rvert^{2}+1})$
(5)

The factor of $\frac{1}{2}$ does not exist here because the entire time slot
will be used for transmission in non-cooperative communication. If we DID NOT
use NOMA, for example, if we use Time-division multiple access (TDMA), we will
allocate half of the time slot for transmission of the far user data. Hence,
the achievable rate of the far user would be,

$\mathbf{R}_{f,OMA}=\frac{1}{2}log_{2}(1+\rho\lvert\mathbf{h}_{f}\rvert^{2})$
(6)

Now that we have seen what cooperative communication is, and how it is useful
to our network, let us design a cooperative NOMA network, that we order the
performances of different schemes as cooperative NOMA $>$ non-cooperative NOMA
$>$ Orthogonal Multiple Access (OMA). So, we can see from Fig. 2 that
cooperative communication is beneficial.

Figure 2: Outage probabilities of the considered transmission schemes:
cooperative NOMA, non-cooperative NOMA, Orthogonal Multiple Access (OMA).

###  III-B User Pairing in NOMA

We have previously shown that NOMA can support numerous consumers in the same
band simultaneously moment. The next logical step will be to figure out how
many users can be supported per frequency carrier. In practice, if the number
of users is increased beyond a certain point, the network’s total throughput
begins to decline. As a result, the number of users per carrier cannot be
increased forever.

A feasible option to support all users is to use hybrid NOMA. Hybrid NOMA is a
technique that combines NOMA and any of the OMA approaches. Consider the
combination of TDMA and NOMA, as indicated in Fig.3. Assume we have a time
slot with a duration of 4ms. Within this time frame, we must support four
users. Now, TDMA will divide the 4 ms slot into four 1 ms slots, and each user
will be assigned one slot. The four users will be given the entire 4 ms slot
by NOMA. This, as we all know, will increase SIC complexity and processing
time. In contrast, hybrid NOMA divides the 4 ms slot into two 2 ms slots and
assigns two NOMA users to each slot. As can be shown, hybrid NOMA can provide
service to all users while reducing complexity. As we progress through this
article, we’ll talk more about hybrid NOMA methods. Now we must figure out how
to match users within each orthogonal resource that is offered. Should we
group users together(1,2),(3,4) or (1,3),(2,4) or (1,4),(2,3).

Figure 3: Hybrid NOMA.

##  IV Performance Analysis

Let us consider a downlink cooperative communication-based UAV scenario with
four users $U_{1},U_{2},U_{3}~{}~{}and~{}U_{4}$. Let
$d_{1},d_{2},d_{3}~{},and~{}d_{4}$ denote the distances of those users,
respectively, from the UAV. $U_{1}$ is the nearest user and $U_{4}$ is the
farthest user. Therefore, their channel conditions are ordered as
$\lvert\mathbf{h_{1}}\rvert^{2}<\lvert\mathbf{h_{2}}\rvert^{2}<\lvert\mathbf{h_{3}}\rvert^{2}<\lvert\mathbf{h_{4}}\rvert^{2}$.
We have two orthogonal resource blocks (time/ frequency/ subcarriers) to which
two users must be allocated. We will pair users based on various distances.
There are two basic ways to accomplish this:

  * •

Near-far pairing (N-F)

  * •

Near-near, far-far pairing (N-N, F-F)

####  IV-1 Near-far pairing (N-F)

In this method, the nearest user to the UAV is paired with the farthest user
from the UAV. The next nearest user is paired with the next farthest user and
so on. In our example, $U_{1}$ is the nearest user and $U_{4}$ is the farthest
user. So, N-F pairing will pair $U_{1}$ with $U_{4}$ in one resource block.
$U_{2}$ will be paired with $U_{3}$ in the next resource block.

In the first pair of users, $U_{1}$ is the near user and $U_{4}$ is the far
user. Therefore, we have to choose the power allocation coefficients as
$\alpha_{1}<\alpha_{4}$. So, $U_{1}$ should perform SIC, while $U_{4}$ will
perform direct decoding. Similarly, in the second pair of users, $U_{2}$ is
the near user and $U_{3}$ is the far user. Therefore, we have to choose
$\alpha_{2}<\alpha_{3}$. Here, $U_{2}$ should perform SIC while $U_{3}$ will
perform direct decoding.

The achievable rates for the users in the first pair are,

$\mathbf{R}_{1,nf}=\frac{1}{2}log_{2}(1+\frac{P\alpha_{1}\lvert
h_{1}\rvert^{2}}{\sigma^{2}})(after~{}SIC).$ (7)

$\mathbf{R}_{4,nf}=\frac{1}{2}log_{2}(1+\frac{P\alpha_{4}\lvert
h_{4}\rvert^{2}}{P\alpha_{1}\lvert h_{4}\rvert^{2}+\sigma^{2}}).$ (8)

Similarly, for the second pair,

$\mathbf{R}_{2,nf}=\frac{1}{2}log_{2}(1+\frac{P\alpha_{2}\lvert
h_{2}\rvert^{2}}{\sigma^{2}})(after~{}SIC).$ (9)

$\mathbf{R}_{3,nf}=\frac{1}{2}log_{2}(1+\frac{P\alpha_{3}\lvert
h_{3}\rvert^{2}}{P\alpha_{2}\lvert h_{3}\rvert^{2}+\sigma^{2}}).$ (10)

The sum rate of the (N-F) scheme will be:

$\mathbf{R}_{nf}=\mathbf{R}_{1,nf}+\mathbf{R}_{2,nf}+\mathbf{R}_{3,nf}+\mathbf{R}_{4,nf}.$
(11)

####  IV-2 Near-near, far-far pairing (N-N, F-F)

Another way to perform user pairing is to group the nearest user with the next
nearest user. The farthest user is grouped with the next farthest user. If we
follow this strategy, in our example, $U_{1}$ will be paired with $U_{2}$ in
one resource block. $U_{3}$ will be paired with $U_{4}$ in the next resource
block.

Now, in the first pair of users, $U_{1}$ is nearest to the BS when compared to
$U_{2}$. Therefore, we have to choose $\alpha_{1}<\alpha_{2}$ . $U_{1}$ should
perform SIC, $U_{2}$ will perform direct decoding. Similarly, $U_{3}$ is
closer to the BS than $U_{4}$. So, we have to choose $\alpha_{3}<\alpha_{4}$.
$U_{3}$ should perform SIC, while $U_{4}$ will perform direct decoding.

The achievable rates for the users in the first pair are,

$\mathbf{R}_{1,nn}=\frac{1}{2}log_{2}(1+\frac{P\alpha_{1}\lvert
h_{1}\rvert^{2}}{\sigma^{2}})(after~{}SIC).$ (12)

$\mathbf{R}_{2,nn}=\frac{1}{2}log_{2}(1+\frac{P\alpha_{2}\lvert
h_{2}\rvert^{2}}{P\alpha_{1}\lvert h_{2}\rvert^{2}+\sigma^{2}}).$ (13)

Similarly, for the second pair,

$\mathbf{R}_{3,nn}=\frac{1}{2}log_{2}(1+\frac{P\alpha_{3}\lvert
h_{3}\rvert^{2}}{\sigma^{2}})(after~{}SIC).$ (14)

$\mathbf{R}_{4,nn}=\frac{1}{2}log_{2}(1+\frac{P\alpha_{4}\lvert
h_{4}\rvert^{2}}{P\alpha_{3}\lvert h_{4}\rvert^{2}+\sigma^{2}}).$ (15)

The sum rate of the (N-F) scheme will be

$\mathbf{R}_{nn}=\mathbf{R}_{1,nn}+\mathbf{R}_{2,nn}+\mathbf{R}_{3,nn}+\mathbf{R}_{4,nn}.$
(16)

##  V Simulation Results

We have observed two different AF relaying UAV cooperative NOMA approaches.
The question is which is better, and what if we just multiplex all of the
users on the same carrier (SC-NOMA), with no user pairing? Given the
additional work required for user pairing, why do we need NOMA at all? Why not
use TDMA instead? Let’s find out by putting everything together in MATLAB. In
this simulation, $N$ users with $N=4$ are considered. When we utilize each of
the user pairing strategies with the UAV that we looked at, we will plot the
network’s sum rate. We will also examine the network’s sum-rate performance
with simply SC-NOMA and TDMA with UAV.

Figure 4: Sum-rate performance of the network with SC-NOMA, TDMA, hybrid NOMA
N-F pairing and hybrid NOMA NN-FF pairing with/without UAV.

When the near user is coupled with the far user, as seen in Fig.4, a higher
sum rate is produced. This supports the well-known notion that cooperative
NOMA operates better when the channel conditions, or the LoS probability model
in UAV communication, are different between the two users. When near-near,
far-far pairing is employed, NOMA-based UAV still outperforms TDMA-based UAV
in terms of sum rate, but the difference is not significant. When compared to
TDMA, the performance of SC-NOMA-based UAVs is poor since all users are
crammed onto the same carrier, generating interference. This also validates
our assumption that increasing the number of customers sharing the same
carrier without paying a premium is impossible. Another benefit of the N-F
pairing UAV relay over the N-N-F-F pairing UAV relay is the careful selection
of users who may be impacted by interference. In contrast to the N-N-F-F
system, which gives interference-free transmission to one near the user and
one far user (assuming perfect SIC), the N-F strategy provides interference-
free reception to both near users (assuming perfect SIC). As a result, N-F has
a higher sum rate. When noise dominates the interference power, however, the
choice of users who experience interference makes a little effect. Both
systems are projected to perform similarly.

##  VI Conclusion and Future Work

In this paper, a cooperative NOMA user pairing under an LoS channel is
proposed, which is provided by a UAV serving as an AF relay. We investigated
two user pairing strategies to improve downlink transmission performance.
According to our results, implementing a user pairing strategy offers the most
appropriate use of power and bandwidth resources. If we couple users at
random, we may not be able to maximize the network’s potential. The conversion
of our single-antenna UAV relay model to a multiple-antenna UAV relay should
be interesting. These issues will be addressed in the future.

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