Energy efficient power control for device to device communication in 5G networks

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 4, August 2020, pp. 4118~4135
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i4.pp4118-4135  4118
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Energy efficient power control for device to
device communication in 5G networks
Mohamed Amine Charar, Zouhair Guennoun
Smart Communications (ERSC formerly known as LEC),
Research Center E3S Ecole Mohammadia d’Ingénieurs, Mohammed V University, Morocco
Article Info ABSTRACT
Article history:
Received Sep 29, 2019
Revised Mar 3, 2020
Accepted Mar 12, 2020
Next generation cellular networks require high capacity, enhanced efficiency
of energy and guaranteed quality of service (QoS). To reach these goals,
device-to device (D2D) communication is a candidate technologie for future
5th Generation especially applications that require the reuse, the hop and
the proximity gain. The present paper studies the energy efficient power
control for the uplink of an OFDMA (orthogonal frequency-division multiple
access) system composed of both regular cellular users and device-to-device
(D2D) pairs. First, we analyze and model mathematically the prerequisites
for D2D communications and classical cellular links in terms of minimum
rate and maximum power requirement. Second, we use fractional programming
in order to convert the initial problem into a concave one and we apply
non-cooperative game theory in order to characterize the equilibrium. Then, we
got the solution of the problem from the results of a water-filling power
allocation. Moreover, we employ a distributed design for power allocation by
means of three methods: a) Theory of fractional programming b) Closed
form expression (the novelty is the use of Wright Omega function).
c) Inverse water filling. Finally, simulations in both static and dynamic
channel setting are realized to demontrate the enhanced gain in term of EE,
SE (spectral efficiency) and time of execution of the iterative algorithm
(Dinkelbach) than the closed form algorithms.
Keywords:
5G
Device-to-device
Energy efficiency
Non-cooperative game theory
OFDMA
Power control
Resource allocation,
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Mohamed Amine Charar,
Research Team in Smart Communications (ERSC formerly known as LEC),
Research Center E3S Ecole Mohammadia d’Ingénieurs,
Mohammed V University,
Avenue Ibn Sina B.P 765, Agdal Rabat 10090 Morocco.
Email: Mohamed.charar@gmail.com
1. INTRODUCTION AND ARTICLE CONTRIBUTIONS
Nowadays, numerous applications of wireless communications has emerged: Mobile broadband,
smart grid, intelligent transportation systems, smart home, E-Health and are using an increasing number of
devices: 18 billions are expected by 2022 [1]. The growth of the mobile internet is estimated to 46 percent
annually from 2017 to 2022 [2] forecasted to reach 77 exabytes (EB) corresponding to a seven-fold increase
every 5 years. The 5G systems are expected by 2020 and according to the wireless industry and academic,
the 1000x capacity of 5G should be obtained using the same amount of energy of the previous cellular
generation. Therefore, 5G have to increase energy efficiency by a factor 1000 or more [3].
From the perspective of the end user, battery autonomy is a decisive element in mobile data
experience [4]. To match with the growth of mobile data, further enhancement in battery technology is
needed. However, battery technology does not develop as quickly [5]. Therefore, improving the EE (energy
efficiency) of mobile communication is essential to lessen the difference between increasing data consumption

Int J Elec & Comp Eng ISSN: 2088-8708 
Energy efficient power control for device to device communication in 5G … (Mohamed Amine Charar)
4119
and the relatively slow progress of battery technology. Although a major advance happens in battery
technology, environmental, economic, and social concerns will continue to be an incentive for upper EE.
In spite of the limited contribution of ICT (information and communication technologies)
to worldwide carbon dioxide emissions of around 2%, CO2 emissions linked to ICTs are growing by 10% per
year [6]. Furtheremore, the energy spending of mobile broadband communications is increasing more rapidly
than the entire ICT. Conquently, more efforts are needed in the area of energy efficient wireless
communications to limit the environmental impact of the mobile broadband communications.
New distributed concepts are emerging such as cognitive radio [7] and device to device
communications (D2D) [8], thanks to smarter equipment (UE). Besides, small cell are massively deployed
and the cell size is continuously reduced which imply less UEs per base station (BS) especially in dense
urban area. The feasibility of power control was studies in [9] is focused on a centralized femtocell network
with a multi-channel user.
Therefore, reducing the processing load at the BS level is convenient and practical for increasing
system scalability and reducing complexity, and distributed decisions should be made at the terminal
level. Power control is of paramount importance in order to mitigate interference in the same channel andthe
near-far effect. The distributed energy efficient power control design give the wireless users the possibility to
benefit from an improved quality of experience by rising their battery life for the same volume of data
consumed while improving access to common resource and reducing complexity managed by the BS.
The D2D (device to device) that was introduced from version 13 of 3GPP [10] is a remarkable
technology which aims to deliver, a direct connection between nearby users, underlying the cellular network.
Thus, D2D improves the EE of the communication. This article discusses energy-efficient power control in
the uplink of a wireless network that offers conventional cellular and D2D communications at the same time.
The D2D communication mode allows nearby users to exchange data bypassing the operators’ cellular
infrastructure, which increases throughput, EE and reduces delay and paves the way to new usages such as
disaster relief [11] and vehicle-to-vehicle communications (V2V) [12]. D2D has better ranges and better
throughput than competitor technologies such as Wifi direct [13].
The technological techniques for resource allocation, interference handling, mobility management
and other system-level component permit proficient D2D functionning [14]. In the vast majortity of
the related literature, D2D communication reuses spectral resource of cellular transmissions, which leads to
more spectrum efficiency. The resource sharing between D2D communication and cellular one poses a major
challenge in term of interference management, the power control is powerful tool to solve this problematic.
In the present paper, we study the uplink of a multi-carrier system with minimum QoS requirement
composed of both cellular and D2D tiers. OFDM technology has severalmerits: low complexity,
compatibility with MIMO. It thusstrongly motivates 5G NR (new radio) still choosing OFDM as the basis of
new waveform design [15]. Consequently, we believe that OFDMA technology will remain a foundation to
5G systems.
The framework of the non-cooperative games is applied to increase the EE. Three methods were
compared for network EE maximization: a) Dinkelbach Method b) Form Expression c) Inverse Water-filling.
As far as we know the comparison between these algorithms exists in the related paper in the literature but
has never been evaluated and assessed.
The main contribution of this article is therefore the implementation and the evaluation of the closed
expression-form algorithm and the use of Wright Omega instead of Lambert-W function. Whereas the closed
expression of power was described in theory in [16], it was not implemented. Graphical comparison of the
EE, SE, power and time execution was done for Dinkelbach and Closed form algorithms forcellular users and
also for D2D users. A baseline algorithmwas used: the Inverse waterfilling using exact minimum rate
requirements.
2. RELATED WORKS
In the work of [17], the EE metric frequently encountered in later work, is defined as the data
successfully transmitted (bit) over the energy consumed (Joule), also authors applied non-cooperative games
theory to increase EE in the case of data transmission. This work was later extended by [18] using a linear
pricing function corresponding to the power that is transmitted. The authors of [19] extended later the work
of [17] to multi-carrier CDMA systems.
The authors of [20] studied the Games of power allocation for MIMO (multiple input multiple
output). The authors of [21] used the potential games to tackle resource allocation for a multicell system
using OFDMA (orthogonal frequency multiple access) technology. The authors of [22] proposed a cross-layer
method for the control of distributed energy efficiency power in networks experiencing interference.

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Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 4118 - 4135
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The authors of [5] study interference-limited environment employing non-cooperative games, then
a trade-off between spectral efficiency and EE is obtained. The authors of [23] apply the concept of repeated
game in order to handle decentralized power control. The uplink of an OFDMA based HetNet (hetegenous
network) composed of a macro cell and helped by a set of small cell was studied by the authors of [24] where
the maximization of EE is done while respecting minimum QoS requirements. They define a solution based
on Debreu Equilibrium that generalizes Nash equilibrium, which leads to a water filling like best response.
The trade-off between spectral efficiency (SE) and energy efficiency (EE) is investigated for device-to-
device (D2D) communications reusing uplink channel of cellular networks in [25].
The rest of the article is structured as following. The notations adopted are defined in the section 3.
The system model is discussed in section 4. In the section 5 the power control game is described, then
the Nash equilibrium solution is obtained using fractional programming theory in section 6. The practical
aspect and the design of the algorithm are given in Section 7. The simulations results are commented in|
the section 8. W conclude finally in section 9.
3. NOTATIONS
The notation in the sequel of the present of the present paper are as follows:
 Bold letters are used for matrices and vectors
 Lambert W function is denoted 𝒲()
 max⁡(0, 𝑥) is denoted 𝑥+
 The vector (𝛼1, … , 𝛼 𝐾) is written more compactly as (𝛼 𝑘, 𝛼−𝑘) with:
𝛼−𝑘 =⁡(𝛼1, … , 𝛼 𝑘−1, 𝛼 𝑘+1, … , 𝛼 𝐾)⁡
 |𝐴| is used for the cardinality of a set⁡𝐴
4. SYSTEM MODEL
The system we adopt is a single cell served by one BS (base station), 𝐾𝑐 cellular UEs and 𝐾𝑑⁡D2D
transmitter/receiver pairs.
The UEs of the system belong to the following set: 𝐾𝑐 = {𝑈𝐸1, 𝑈𝐸2, … , 𝑈𝐸 𝐾 𝑐
}
The set of D2D pairs in the system is denoted as: 𝐾𝑑 = {𝐷1, 𝐷2, … , 𝐷 𝐾 𝑑
}
Spectral resource allocation is based on the OFDMA technology. One sub-carrier is allocated to
each cellular user. Therefore, no interference exists in the cellular tier. By contrary, every D2D transmitter
has the freedom to transmit on all sub-carriers. We assume no sub-carrier allocation to D2D pairs is
performed, but rather D2D transmit randomly their powers over the subcarriers. The Table 1 gives the main
notations adopted.
Table 1. Notations
Notation Meaning
W Bandwidth of the system
Kc Number of equally spaced sub-carriers
B =
W
Kc
Bandwidth per sub-carrier
hk Channel between the cellular k and the BS
hk,l Channel between the cellular k and the D2D receiver l
gi
n
Channel of the D2D pair i
gi,c
n
Channel over the Sub-Carrier n between The BS and the D2D transmitter i
gi,l
n
Channel over the sub-carrier n between the D2D receiver l and the D2D transmitter i
pk
c
Power of the cellular k
pi,n
d D2D Power of transmitter i over the sub-carrier n
pmax
c Cellular maximum power
pmax
d D2D maximum power
pcir
c Cellular circuit power
pcir
d D2D circuit power
Rmin
c
Cellular minimum rate
Rmin
d D2D minimum rate

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Thus, the baseband signal is written for UEk as:
yk
c
= hk√pk
c
xk
c
+ ∑ gi,c⁡
k
√pi,k
d
xi,k
dKd
i=1 + nk (1)
With nk is an AWGN (Additive White Gaussian Noise) that is having a mean 0 and variance σ2
. Similarly,
the signal of the baseband is written for D2Dk in the sub-carrier n as:
yk
d
= gk
n
√pk,n
d
xk,n
d
+ ∑ gi,c⁡
k
√pi,k
d
xi,k
dKd
i=1,i≠k + hn√pn
c + nk,n⁡ (2)
with nk,n is an AWGN having the same mean and variance as nk.
The channels are assumed complex Gaussian: the real part and imaginary part are both Gaussians.
For each D2D pair i, its power vector is denoted (over all subcarriers):
𝐩𝐢
𝐝
=
[𝐩𝐢,𝟏
𝐝
, 𝒑𝒊,𝟏
𝒅
, … , 𝒑𝒊,𝑲 𝒅
𝒅
]⁡ (3)
and the vector power of D2D pairs excluding the D2D pair 𝑖 is:
𝐩−𝐢
𝐝
=
[𝐩 𝟏
𝐝
, … , 𝒑𝒊−𝟏
𝒅
, 𝒑𝒊+𝟏
𝒅
, … , 𝒑 𝑲 𝒅
𝒅
]⁡ (4)
The transmitting vector corresponding to all D2D pairs:
𝐩 𝐝
= [𝐩𝐢
𝐝
, 𝐩−𝐢
𝐝
]⁡ (5)
The total power of D2D pair 𝑖⁡accounting the circuit related consumption:
𝑃𝑡𝑜𝑡(𝒑𝒊
𝒅
) = ∑ 𝑝𝑖,𝑛
𝑑
+ 𝑝𝑐𝑖𝑟
𝑑𝑲 𝒅
𝒏=𝟏 (6)
and the power vector of UEs except UEk is:
𝐩−𝐢
𝐝
=
[p1
c
, … , 𝑝 𝑘−1
𝑐
, 𝑝 𝑘+1
𝑐
, … , 𝑝 𝐾 𝑐
𝑐
]⁡ (7)
The global power of cellular users is denoted:
𝐩 𝐜
= [pk
c
, 𝐩−𝐤
𝐜
]⁡ (8)
The total power of UEk accounting the circuit related consumption:
⁡𝑃𝑡𝑜𝑡(𝑝 𝑘
𝑐
) = 𝑝 𝑘
𝑐
+ 𝑝𝑐𝑖𝑟
𝑐
⁡ (9)
The SINR (Signal to Interference plus Noise Ratio) of UEk at the nth
sub-carrier:
𝛾 𝑘
𝑐
=
|ℎ 𝑘|2 𝑝 𝑘
𝑐
∑ |𝑔𝑖,𝑐
𝑘
|
2
𝑝𝑖,𝑘
𝑑
+𝜎2𝐾 𝑑
𝑖=1
⁡ (10)
We denote the SINR of D2D pair 𝑘 at the nth
sub-carrier:
𝛾 𝑘,𝑛
𝑑
=
|𝑔 𝑘
𝑛
|
2
𝑝 𝑘,𝑛
𝑑
∑ |𝑔𝑖,𝑘
𝑛
|
2
𝑝𝑖,𝑛
𝑑
+|ℎ 𝑛|2 𝑝 𝑛
𝑐 +𝜎2𝐾 𝑑
𝑖=1
𝑖≠𝑘
⁡ (11)
Let us define the effective channel gain of the cellular user UEk as following:
𝜈 𝑘
𝑐
=
|ℎ 𝑘|2
∑ |𝑔𝑖,𝑐
𝑘
|
2
𝑝𝑖,𝑘
𝑑
+𝜎2𝐾 𝑑
𝑖=1
⁡ (12)

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The effective channel gain of D2D pair k is defined as:
𝜈 𝑘,𝑛
𝑑
=
|𝑔 𝑘
𝑛
|
2
∑ |𝑔𝑖,𝑘
𝑛
|
2
𝑝𝑖,𝑛
𝑑
+|ℎ 𝑛|2 𝑝 𝑛
𝑐 +𝜎2𝐾 𝑑
𝑖=1
𝑖≠𝑘
⁡ (13)
The SE (Spectral Efficiency) utility function of UEk is:
𝑆𝐸 𝑘
𝑐
(𝑝 𝑘
𝑐
, 𝐩−𝐤
𝐜
, 𝐩 𝐝
) = log2 (1 +
𝛾 𝑘
𝑐
Γ
)⁡ (14)
With Γ the SINR gap w.r.t BER as defined in [26]:Γ = −
ln(5BER)
1.5
The SE (Spectral Efficiency) utility function of the kth
D2D pair is:
𝑆𝐸 𝑘
𝑑
(𝒑 𝒌
𝒅
, 𝒑−𝒌
𝒅
, 𝒑 𝒄
) = ∑ 𝑙𝑜𝑔2 (1 +
𝛾 𝑘,𝑛
𝑑
𝛤
)
𝐾 𝑐
𝑛=1 ⁡ (15)
The EE (Energy Efficiency) utility function of the uplink communication of UEk is:
𝐸𝐸 𝑘
𝑐
(𝑝 𝑘
𝑐
, 𝒑−𝒌
𝒄
, 𝒑 𝒅
) =
𝑺𝑬 𝒌
𝒄
(𝑝 𝑘
𝑐
,𝒑−𝒌
𝒄
,𝒑 𝒅)
𝑷 𝒕𝒐𝒕(𝑝 𝑘
𝑐
)
(16)
The EE (Energy Efficiency) utility function of the kth
D2D pair:
𝐸𝐸 𝑘
𝑑
(𝒑 𝒌
𝒅
, 𝒑−𝒌
𝒅
, 𝒑 𝒄
) =
𝑺𝑬 𝒌
𝒅
(𝒑 𝒌
𝒅
,𝒑−𝒌
𝒅
,𝒑 𝒄)
𝑷 𝒕𝒐𝒕(𝒑 𝒊
𝒅)
⁡ (17)
Each UE will increase its energy efficiency and at the same time respect the minimum rate
requirements. The UEk will try to maximize its global energy efficiency while respecting minimum rate
constraint. Consquently, the following problem has to be solved for UEk:
Maximize⁡ 𝑝 𝑘
𝑐 𝐸𝐸 𝑘
𝑐
(𝑝 𝑘
𝑐
, 𝒑−𝒌
𝒄
, 𝒑 𝒅
)
s.t.(C1A)⁡pk
c
∈ [0, pmax
c ] (18)
(C2A) 𝑆𝐸 𝑘
𝑐
(𝑝 𝑘
𝑐
, 𝒑−𝒌
𝒄
, 𝒑 𝒅
) ≥ 𝑅 𝑚𝑖𝑛
𝑐
Similarly, the following problem has to be solved for kth
D2D pair:
Maximize 𝒑 𝒌
𝒅
⁡
𝐸𝐸 𝑘
𝑑
(𝒑 𝒌
𝒅
, 𝒑−𝒌
𝒅
, 𝒑 𝒄
)
s.t.(C1B)⁡𝒑 𝒌
𝒅
∈ [0, 𝑝 𝑚𝑎𝑥
𝑑 ] 𝐾 𝑐 (19)
(C2B) 𝑆𝐸 𝑘
𝑑
(𝒑 𝒌
𝒅
, 𝒑−𝒌
𝒅
, 𝒑 𝒄
) ≥ 𝑅 𝑚𝑖𝑛
𝑑
Hence, there are (𝐾𝑐 + 𝐾𝑑) optimization problems to be jointly solved in the system. The multivariate
optimization cannot be applied; as there is no central entity that is controlling all variables of the system,
instead each has partial control on his own variables, which leads to the application of Game Theory.
5. GAME THEORY FORMULATION
The non-cooperative game theory appears as the natural tool to tackle to problem as we have
independent, rational players that control their variables. The following triplet defines the non-cooperative
game formally as:
𝒢 = {𝒦, {𝓐 𝑘} 𝑘∈{1,..,𝐾}, {𝒰 𝑘} 𝑘∈{1,..,𝐾}}
where :
 Players: 𝐾⁡ =⁡{𝐾𝑐, 𝐾𝑑}
 Actions: 𝒜 𝑘 = [0, 𝑝 𝑚𝑎𝑥
𝑐 ]⁡𝑖𝑓𝑘 ∈ 𝐾𝑐⁡⁡𝑜𝑟⁡[0, 𝑝 𝑚𝑎𝑥
𝑑 ] 𝐾𝑐⁡𝑖𝑓⁡𝑘 ∈ 𝐾 𝑑
 Utilities: The EE utility function (bit/J/Hz).
For each 𝑘 ∈ {1, . . , 𝐾} the action space corresponding to other players:⁡ 𝒜−𝑘 = ∏ 𝒜𝑖
𝐾
𝑖=1
𝑖≠𝑘

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In order to get a solution, we have to find the equilibrium of the game. Indeed, the importance of
the equilibrium is game is important as agents will become predictable and stable. The network operates
effectively when reaching equilibrium [27]. The power strategy of each user must be feasible by respecting
minimum QoS (quality of service) conditions. This leads us to use a different Game theory solution
that allows each player to choose its strategy knowing the strategy of other players while respecting
feasibility conditions: the GNE (generalized Nash equilibrium). By supposing that each player’s strategy may
depend on the other players’, the GNE goes beyond the common NE (Nash equilibrium) [28]. Not only
utility functions are dependent but also the players’ action sets are couple, which is different from classical
non-cooperative games [29].
We will denote by 𝒜 𝑘
𝐺
(𝐩−𝑘) the set of user UEk actions respecting the minimum rate and
the maximum power requirements with respect to other players’ strategy:
𝒜 𝑘
𝐺(𝐩−𝑘) = {𝐩 𝑘 ∈ 𝒜 𝑘:⁡(𝐩 𝑘, 𝐩−𝑘)⁡respects⁡(C1)⁡&⁡C2)}
Definition. Generalized Nash equilibrium
The matrix of power 𝐩∗⁡
= (𝐩 𝑘
∗
, 𝐩−𝑘
∗
) is a Generalized Nash equilibrium if:⁡𝐩−𝑘
∗
∈ 𝒜 𝑘
𝐺
(𝐩−𝑘) and:
∀pk ∈ 𝒜k
G
(𝐩−k
∗ ): EEk(𝐩∗) ≥ EEk(𝐩 𝐤, 𝐩 𝐤
∗
) (20)
Which means that no player has the interest to deviate unilaterally from a GNE to another feasible point
(respecting the constraints).
6. FRACTIONAL PROGRAMMING FORMULATION AND GAME SOLUTION
The EE objective function in (18) and (19) are fractional and non-convex. The most suitable
mathematical framework to tackle the optimization of such functions is the fractional programming, which
provide polynomial complexity algorithms when the numerator is concave and denominator is convex [29].
First, let us give a formal definition of Fractional programming:
Definition. Fractional programming
Let 𝐶 ∈ ℝ 𝑛
convex subset and let 𝐶 →⁡ ℝthe following functions:
𝑓: 𝑥 → 𝑓(𝑥) and g: 𝑥 → 𝑔(𝑥) (21)
A fractional program is the optimization problem:
Maximizex∈C
f(x)
g(x)
(22)
The fractional programming allows us to solve equivalent form of the problem that is less complex than
the original one:
Proposition 1. The vector⁡𝑥∗
∈ 𝐶⁡solves the (22) if and only if: (𝑓(𝑥∗) − 𝜆∗
𝑔(𝑥∗)) = 0, 𝜆∗
the zero of
the function:
𝐻(𝜆) = 𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝑥∈𝐶⁡𝑓(𝑥) − 𝜆𝑔(𝑥) (23)
For a given value, 𝜆 𝑘
𝑐
the UEk will solve the following transformed fractional programming problem
instead of (18):
Maximize⁡pk
c SEk
c
(pk
c
, 𝐩−𝐤
𝐜
, 𝐩 𝐝
) − λk
c
Ptot(pk
c
)
s.t. (C1A)& (C2A) (24)
For the optimal value, 𝜆 𝑘
𝑐,∗
we get:
SEk
c
(pk
c
, 𝐩−𝐤
𝐜
, 𝐩 𝐝
) − λk
c,∗
Ptot(pk
c
) = 0 (25)
This means that the best response power 𝑝 𝑘
𝑐,∗
given other transmitters (𝒑−𝒌
𝒄
, 𝒑 𝒅
)⁡verifies the following
expression:

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4124
𝜆 𝑘
𝑐,∗
=
𝑆𝐸 𝑘
𝑐
(𝑝 𝑘
𝑐
,𝒑−𝒌
𝒄
,𝒑 𝒅)
𝑃 𝑡𝑜𝑡(𝑝 𝑘
𝑐
)
(26)
Similarly, for a given value 𝜆 𝑘
𝑑
the equivalent problem of optimization that the kth
D2D pair will solve instead
of (19):
Maximize⁡ 𝐩 𝐤
𝐝 𝑆𝐸 𝑘
𝑑
(𝒑 𝒌
𝒅
, 𝒑−𝒌
𝒅
, 𝒑 𝒄
) − 𝜆 𝑘
𝑑
𝑃𝑡𝑜𝑡(𝒑 𝒌
𝒅
)
s.t. (C1B)& (C2B) (27)
For the optimal value, λk
d,∗
we get:
𝑆𝐸 𝑘
𝑑
(𝒑 𝒌
𝒅
, 𝒑−𝒌
𝒅
, 𝒑 𝒄
) − 𝜆 𝑘
𝑑,∗
𝑃𝑡𝑜𝑡(𝒑 𝒌
𝒅
) = 𝟎 (28)
This means that the best response power 𝒑 𝒌
𝒅
given other transmitters (𝒑−𝒌
𝒅
, 𝒑 𝒄
)verifies the following
expression:
λk
d,∗
=
SEk
d
(𝐩 𝐤
𝐝
,𝐩−𝐤
𝐝
,𝐩 𝐜)
𝐏 𝐭𝐨𝐭(𝐩 𝐤
𝐝)
(29)
Practically, the solution of (24) and (27) is got when the players are giving their best response
to each other. As follows, the kth
user best response function which is the best action 𝐩 𝐤 to adopt when
the strategy of the other players is 𝐩−𝐤 ∈ 𝒜−𝑘
𝐺
(𝑝 𝑘)⁡:
𝐵𝑅 𝑘(𝒑−𝒌) = 𝒂𝒓𝒈𝒎𝒂𝒙 𝒑 𝒌∈𝓐 𝒌
𝑮(𝒑−𝒌)⁡𝑬𝑬(𝒑 𝒌, 𝒑−𝒌) (30)
The necessary conditions are more easily obtained in the case of a single carrier.
Proposition 2. In the single carrier case, the uniqueness and existence of Generalized Nash equilibrium are
guaranteed, as the BR functions of the user k are standard and verify the following properties:
 Concavity: 𝐵𝑅 𝑘(𝒑−𝒌) is concave strictly in 𝒑−𝒌
 Positivity: 𝐵𝑅 𝑘(𝒑−𝒌) > 0
 Monotonicity: if 𝑞−𝑘 > 𝑝−𝑘 then 𝐵𝑅 𝑘(𝒒−𝒌) > 𝐵𝑅 𝑘(𝒑−𝒌)
 Scalability: For all 𝛼 > 1, 𝛼𝐵𝑅 𝑘(𝑝−𝑘) > 𝐵𝑅 𝑘(𝛼𝑝−𝑘)
Proof. The proof is given in [30]
In the case of multi-carrier, the equilibrium can be charactrized by giving the expression of power for each
sub-carrier and for each user as in the following proposition.
Proposition 3. The best power of the UEk is given by the following when the game admits a GNE:
pk
c,∗
= min (pmax
c
, (
1
λk
c,∗ −
1
νk,n
c )
+
) (31)
The optimal value 𝜆 𝑘
𝑐,∗
:
𝜆 𝑘
𝑐,∗
= min( 𝜆 𝑘
𝑐
, 𝜆 𝑘
𝑐,𝑚𝑎𝑥
) (32)
𝜆 𝑘
𝑐
=
𝜔(log(𝐵 𝑘
𝑐
exp(𝐴 𝑘
𝑐
−1)))
𝐵 𝑘
𝑐 (33)
With: 𝐴 𝑘
𝑐
= log⁡( 𝜈 𝑘
𝑐
) (34)
𝐵 𝑘
𝑐
= (𝑝𝑐𝑖𝑟
𝑐
−
1
𝜈 𝑘,𝑛
𝑑 ) (35)
𝑝 𝑚𝑎𝑥
𝑐
is calculated when the rate requirements are active:
𝜆 𝑘
𝑐,𝑚𝑎𝑥
= 𝜈 𝑘,𝑛
𝑐
2−𝑅 𝑚𝑖𝑛
𝑐
(36)

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Proposition 4. The following formula gives the optimal power of the D2Dk for each sub-carrier n:
∀𝑛 ∈ 𝐾𝑐: 𝑝 𝑘,𝑛
𝑑,∗
= min (𝑝 𝑚𝑎𝑥
𝑑
, (
1
𝜆 𝑘
𝑑,∗ −
1
𝜈 𝑘,𝑛
𝑑 )
+
) (37)
The optimal value 𝜆 𝑘
𝑑,∗
:𝜆 𝑘
𝑑,∗
= min⁡( 𝜆 𝑘
𝑑
, 𝜆 𝑘
𝑑,𝑚𝑎𝑥
) (38)
𝜆 𝑘
𝑑
=
𝜔(log(𝐵 𝑘 exp(𝐴 𝑘
𝑑
−1)))
𝐵 𝑘
𝑑 (39)
𝑁𝑘 is the set of active subcarriers (i.e. with strictly positive power) of D2Dk when:
𝜆 𝑘
𝑑,∗
=⁡ 𝜆 𝑘
𝑑
𝑁𝑘
∗
= {𝑛 ∈ 𝐾𝑐| (
1
𝜆 𝑘
𝑑 −
1
𝜈 𝑘,𝑛
𝑑 ) > 0} (40)
with:
𝐴 𝑘
𝑑
=
log(∏ 𝑛∈𝑁 𝑘
∗
𝜈 𝑘,𝑛
𝑑
)
|𝑁 𝑘
∗|
(41)
𝐵 𝑘
𝑑
=
(𝑝 𝑐𝑖𝑟
𝑑
−∑
1
𝜈 𝑘,𝑛
𝑑𝑛∈𝑁 𝑘
∗ )
|𝑁 𝑘
∗|
(42)
When the normalized rate constraints are active, 𝜆 𝑘
𝑑,𝑚𝑎𝑥
has the following formulation
𝜆 𝑘
𝑑,𝑚𝑎𝑥
= (2−|𝑁 𝑘
max
|𝑅min
𝑑
∏ 𝜈 𝑘,𝑛
𝑑
𝑛∈𝑁 𝑘
max )
1
𝑁 𝑘
max
(43)
with:𝑁𝑘
max
= {⁡n ∈ Kc| (
1
λk
d,max −
1
νk,n
d ) > 0}⁡ (44)
Proof. The proof is given in [31] applied to our model.
7. ALGORITHMS DESIGN AND ANALYSIS
7.1. Practical design
From practical point of view, each player give its best response to solve EE maximization problem
knowing other players’ strategy. We consider a scheme that is asynchronous as the user chooses its power
strategy at time 𝑡 based on the other players’ strategy of 𝑡 − 1. Indeed, an instantaneous feeback is is very
difficult in practice. We consider that each player knows his individual CSI (channel state information)
whithin the SINR on each subcarrier. As intial value, the cellular users choose 𝑝𝑖𝑛𝑖𝑡
𝑐
and D2D users
choose⁡𝑝𝑖𝑛𝑖𝑡
𝑑
. In each iteration, the user UEk estimates the effective channel gain based on the previous value
and based on the previous SINR:
𝜈 𝑘
𝑐̂(𝑡) =
𝛾 𝑘
𝑐(𝑡−1)
𝑝 𝑘
𝑐(𝑡−1)
(45)
The user UEk estimates the EE as following and tries to increase it w.r.t to conditions (C1A) and (C2A):
𝐸𝐸 𝑘
𝑐̂ (𝑡) =⁡
𝑆𝐸 𝑘
𝑐̂ (𝑡)
𝑝 𝑘,𝑛
𝑐 (𝑡)+𝑝 𝑐𝑖𝑟
𝑐 (46)
with:
𝑆𝐸 𝑘
𝑐̂ (𝑡) = log2(1 + 𝜈 𝑘
𝑐̂(𝑡)𝑝 𝑘
𝑐⁡
(𝑡)) (47)

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As in proposition 3:
𝑝 𝑘
𝑐,∗⁡
(𝑡 + 1) = min (𝑝 𝑚𝑎𝑥
𝑐
, (
1
𝜆 𝑘
𝑐,∗
(𝑡+1)
−
1
𝜈 𝑘
𝑐̂(𝑡+1)
)
+
) (48)
Similarly, the user D2Dk estimates the EE as following and tries to increase it w.r.t to conditions (C1A)
and (C2A):
𝜈 𝑘,𝑛
𝑑̂ (𝑡) =
𝛾 𝑘,𝑛
𝑑 (𝑡−1)
𝑝 𝑘,𝑛
𝑑 (𝑡−1)
(49)
The user D2Dk estimates the EE as following and tries to increase it w.r.t to conditions (C1B) and (C2B):
𝐸𝐸 𝑘
𝑑̂(𝑡) =
𝑆𝐸 𝑘
𝑑̂
(𝑡)
∑ 𝑝 𝑘,𝑛
𝑑 (𝑡)+𝑝 𝑐𝑖𝑟
𝑑𝐾 𝑐
𝑛=1
(50)
with:
𝑆𝐸 𝑘
𝑑̂ (𝑡) = ∑ log2(1 + 𝜈 𝑘,𝑛
𝑑̂ (𝑡)𝑝 𝑘,𝑛
𝑑
(𝑡)
𝐾 𝑐
𝑛=1 (51)
As in proposition 4:
∀𝑛 ∈ 𝐾𝑐: 𝑝 𝑘,𝑛
𝑑,∗
(𝑡) = 𝑚𝑖𝑛 (𝑝 𝑚𝑎𝑥
𝑑
, (
1
𝜆 𝑘
𝑑,∗
(𝑡)
−
1
𝜈 𝑘,𝑛
𝑑̂
(𝑡)
)
+
) (52)
The challenge here is to find 𝜆 𝑘
𝑐,∗
(𝑡)⁡and⁡𝜆 𝑘
𝑑,∗
(𝑡), for that we use three methods:
 The first method that was used by [24] relies on Dinkelbach algorithm: algorithm 3.
 The second method is the proposed in this article relies on the closed form expression as in algorithm 4.
 The third method is the IWF (Inverse Water Filling) is applied using algorithm 2 which aims to have
exactly the minimum normalized SE (Rmin).

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7.2. Complexity analysis
The different algorithms comprise two loops: the inner and the outer loop. Superlinearty
convergence characterize the outer loop iteration; hence, the inner loop dominate the two-loop power control
algorithm [32]. The complexity of the Dinkelbach algorithm is calculatedas 𝑂((𝐾𝑐𝐾𝑑⁡ + ⁡𝐾𝑐)3.5
)
the algorithm IWF has superlinear convergence properties both in the inner and in the outer loops.
For the Closed-Form algorithm we use the Wright-Omega [33] and the Lambert-W functions embedded [34]
in MATLAB Software.
7.3. Convergence analysis
The methods used to calculate the value of λ are iterative (algorithm 1), which leads to
an increasing serie of λ and converge to an optimal value. The algorithms got the optimal power value for
a given λ, which is obtained from previous power values. The algorithms stop running whenever the resultant
is relatively small [31]. The algorithms used are asynchronous: using the past strategy of the competitor in
order to react to take a present action. The repeated games framework guarantees the convergence in
the long-term. Indeed, players decide about the future based on their experiences in the previous stages of
the game [23].
8. SIMULATION AND COMMENTS
For simulations, we choose a single cell system having a radius of 300 meters radius and Kc=3
cellular users and Kd=5 D2D pairs. The Algorithm 4 was implemented using two different ways using
Lambert-W function and Wright-Omega function.
In the figures, we adopt the following notations:
 DBK for the algorithm 3 (Dinkelbach)
 CF for the algorithm 4 (Closed form) with Lambert-W function
 CFOM for the algorithm 4 (Closed form) with Wright Omega function
 IWF for the algorithm 2 for Inverse Water-filling algorithm
We choose 𝑝𝑖𝑛𝑖𝑡 ⁡= ⁡1| 𝑁
𝑝max
2
to initiate power for all users
8.1. Static channel: number of iterations
The Table 2 presents main parameters adopted for the simulations to compare algorithm 3
(Dinkelbach) and algorithm 4 (Closed-form). The channel gain is expressed using the parameter 𝛼 to
calculate the path-loss: ℎ =
𝑐𝑡𝑒
𝑑 𝛼
Table 2. List of parameters
Notation Meaning Value
Kc Number of Cellular users 3
Kc Number of D2D pairs 5
N0 Spectral density of Noise 3.98 × 10−19
W/Hz
B Bandwidth for each subcarrier 1M Hz
𝑝𝑐𝑖𝑟
𝑐 Circuit consumption power for cellular 300mW
𝑝𝑐𝑖𝑟
𝑑 Power circuit for D2D 200mW
𝑝 𝑚𝑎𝑥 Maximum power per sub-carrier 2W
𝛼 𝑐 Propagation exponent for cellular 3.6
𝛼 𝑑 Propagation exponent for cellular 2
cte Constant of propagation 2.57399 x 10−
2
𝑑 𝑚𝑖𝑛
𝑐 Min. Distance form BS 20 meters
𝑑 𝑚𝑖𝑛
𝑑 Min. between D2D 5 meters
𝑅 𝑀𝑖𝑛
𝑐 Min. Normalized Rate for Cellular 0.2 bit/s/Hz
𝑅 𝑀𝑖𝑛
𝑑 Min. Normalized Rate for D2D 2 bit/s/Hz
Γ The BER Gap in the SINR 1
We perform 10 iterations to be sure that the users will end by learning equilibrium by executing
the interative algorithms 3 and 4. We average the energy efficiency, spectral efficiency and power
per subcarrier of the reference user to obtain the results of simulation. The position of the reference user
was averaged 100 times. The simultations show that equilibrium of the game exist and converge before
the 10th iteration. We do not have a saturated equilibrium, which does not reach the maximum power.
For the subsequent simulations, static and flat fading channel model was choosen. The algorithm 3 and 4 both
respect minimum SE and consequently converge to a solution that respect the minimum rate requirement.

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The IWF (inverse water filling) algorithm respects the minimum rate requirement by its construction
to achieve the minimum rate exactly. Hence, the conditions in the maximization problem (C1) and (C2) are
respected. The flat fading assumption leads to the users to choose the same power over all the sub-carriers.
We give below our comments on results obtained:
EE: The Energy efficiency corresponds to the EE utility times the bandwidth.
 Cellular: The simulations in Figure 1 that represent the EE of a reference cellular user shows that
the DBK algorithm is 800% better than CF algorithm. CFOM algorithm realizes the same EE as CF
algorithm. Unsurprisingly, the IWF algorithm has the least EE.
 D2D: The simulations in Figure 2 that represent the EE of a reference D2D user shows that the DBK
algorithm is 20% better than CF algorithm. CFOM algorithm realizes the same EE as CF algorithm.
The IWF algorithm has the least EE.
 Comparison Cellular and D2D: The D2D communication brings proximity ga in, the EE of D2D is at
least 2.5 times the EE of Cellular.
Figure 1. Comparison in term of EE, celluler user
static channel
Figure 2. Comparison in term of EE Static,
D2D user static channel
SE: the SE expressed in bit/s/Hz
 Cellular: the simulations in Figure 3 that represent the SE of a reference cellular user shows that the DBK
 D2D: the simulations in Figure 4 that represent the EE of a reference D2D user shows that the DBK
algorithm is slightly better than CF algorithm. CFOM algorithm realizes the same EE as CF algorithm.
 Comparison Cellular and D2D: The D2D communication brings proximity gain; the SE of D2D is at least
8.3 times the SE of Cellular.
Power: the instant consumed power
 Cellular simulations in Figure 5, that represent the power of a reference Cellular user shows that
the CF algorithm consumes 75% more power than DBK algorithm. The IWF algorithm consumes
the least power.
 D2D simulations in Figure 6, that represent the power of a reference Cellular user shows that
the CF algorithm consumes 25% more power than DBK algorithm. The IWF algorithm consumes
the least power.
Time of execution: simulations in Figure 7 shows the cumulative time of execution expressed in
millisecond. In the 6 first iterations the DBK algorithm consumes more time than the CF algorithm.
The CFOM algorithm based on Wright Omega function has slightly less complexity than Lambert W function
starting from the seventh iteration the DBK algorithm join the closed form. The least time execution belongs
to IWF algorithm.

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Figure 3. Comparison in term of SE,
celluler user static channel
Figure 4. Comparison in term of SE static,
Figure 5. Comparison in term of power,
cellular user static channel
Figure 6. Comparison in term of power static,
Figure 7. Comparison in term of time execution

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8.2. Rayleigh channel: number of iterations
In this subsection, we perform simulations with Rayleigh channel instead of static channel
considered for the previous simulations. For the subsequent simulations, static and flat fading channel model
was choosen. The algorithm 3 and 4 both respect minimum SE and consequently converge to a solution that
respect the minimum rate requirement.
The IWF (inverse water filling) algorithm respects the minimum rate requirement by its construction
to achieve the minimum rate exactly. Hence, the conditions in the maximization problem (C1) and (C2) are
respected. The flat fading assumption leads to the users to choose the same power over all the sub-carriers.
We give below our comments on results obtained:
EE: The Energy efficiency corresponds to the EE utility times the bandwidth.
 Cellular the simulations in Figure 8 that represent the EE of a reference cellular user shows that
the DBK algorithm is 800% better than CF algorithm. CFOM algorithm realizes the same EE as CF
algorithm. Unsurprisingly, the IWF algorithm has the least EE.
 D2D the simulations in Figure 9 that represent the EE of a reference D2D user shows that the DBK
 Comparison cellular and D2D: The D2D communication brings proximity gain, the EE of D2D is at
least 2:25 times the EE of Cellular.
Figure 8. Comparison in term of EE,
cellular user rayleight channel
Figure 9. Comparison in term of EE static,
D2D user rayleight channel
SE: the SE expressed in bit/s/Hz
 Cellular the simulations in Figure 10 that represent the SE of a reference cellular user shows that the CF
algorithm is 20% better than DBK algorithm. CFOM algorithm realizes the same EE as CF algorithm.
 D2D The simulations in Figure 11 that represent the EE of a reference D2D user shows that the DBK
algorithm is slightlly better than CF algorithm. CFOM algorithm realizes the same EE as CF algorithm.
 Comparison cellular and D2D: The D2D communication brings proximity gain, the EE of D2D is at least
8.33 times the EE of Cellular.
Time of execution: simulations in Figure 12 shows the cumulative time of execution expressed in millisecond.
In this case the DBK algorithm has clearly better complexity than the CF algorithms.
Robustness: Although the algorithms DBK and CF do not converge but they show a good robustness as
the power, EE, SE are smooth, stable, and slowly varying after few iterations.

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Figure 10. Comparison in term of SE,
cellular user rayleight channel
Figure 11. Comparison in term of SE static,
D2D user rayleight channel
Figure 12. Time of execution rayleigh
8.3. Impact of minimum rate
In this subsection, we examine the impact of the minimum rate constraint on the SE and EE of
the D2D pair. We take a fixed minimum rate constraint for cellular users 𝑅⁡𝑚𝑖𝑛
𝑐
= ⁡0.2⁡𝑏𝑖𝑡/𝑠/𝐻𝑧 and we vary
the minimum rate for the D2D users from 0 to 30bit/s/Hz As in Figure 13 and Figure 14:
 EE: The energy efficiency grows steadily for IWF algorithm until around 7 bit/s/Hz while it is higher for
the other algorithm. Then the EE drops beyond 10bit/s/Hz for all algorithms.
 SE: The spectral efficiency steadily for IWF algorithm until around 10 bit/s/Hz while it is higher for
the other algorithm. Then the SE is stable beyond 15 bit/s/Hz for all algorithms.

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Figure 13. Comparison of SE as function of
minimum rate
Figure 14. Comparison of EE as function of
minimum rate
9. CONCLUSION AND FUTURE WORKS
In this work, we compare closed-form expression of the power allocation to Dinkelbach algorithm
applied to a D2D communication underlaying a cellular network. Results shows that Dinkelbach algorithm
provides more EE than the Closedform algorithm. Furthermore, in term of SE Dinkelbach algorithm is
equivalent to closed form when it comes to D2D communication type. However, closed form algorithm
outperforms the Dinkelbach for cellular type communication, which is in line with our previous work [31].
In term of time of execution, we remark that the Dinkelbach Algorithm suffers from a slow start in the first
iterations then it becomes faster than closed form algorithms. Due to to its simplicity, the Inverse Waterfilling
algorithm has excellent convergence, lowpower and complexity properties but its EE is very limited and its
SE is just the minimum rate. This algorithm (IWF) can be applied to fixed rate application that are not
ambitious in term of rate: for instance VoNR (voice over new radio). Despitethe non-convergence in
the dynamic channel conditions, the proposed algorithms show good stability and very similar results to
the static case in terms of EE, SE, and power. However, the time of execution in the dynamic case of
Dinkelbach algorithm is much lower than closed form. Although the usage of the Wright Omega function
compared to Lambert Wfunction brought some reduction of time execution, but it was not enough to get
better results than Dinkelbach algorithm was. Whatever the used algorithms, thanks to proximity and
hopgain, D2D communication bring significant improvement of energy efficiency compared to cellular
communication. Yet, more investigation needs to be done in order to increase Energy Efficiency. Another
possible future work is the application to more sophisticated scenarios such as MIMO and Small Cells.
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BIOGRAPHIES OF AUTHORS
Mohamed Amine Charar received the Dipl.-Ing. Degree from EcoleNationaleSupérieure
de Télécommunications Bretagne -France With Major in wireless Communication, in 2008
after a consultantposition in BT Services in Paris France, hejoined Maroc Telecom as
Networks Development Engineer in 2010, from 2015 to present he is the core network
engineering Manager at the same company. He is currently pursuing the Ph.D. degree
atÉcoleMohammadia d’ Ingénieurs (EMI) where he is member of the ERSC formerly known
as LEC, working on Energy Efficiency Power control for Wireless Networks.
Zouhair Guennoun received the B.Eng. degree in electrical engineering in 1987 from ULG,
Liege Belgium, M.Sc. degrees in electrical and computer engineering in 1993 from
ÉcoleMohammadia d’ Ingénieurs (EMI), Rabat, and the Ph.D. degree in applied science in
1996 from EMI. He is currently a Professor of Computer and electronic Engineering at
ÉcoleMohammadia d’ Ingénieurs (EMI) where he is member of the ERSC formerly known
as LEC, and member of the spatial research center (CRES). His research interests include
wireline and wireless networks, advanced communications systems, signal Processing, audio
and image compression, audio cryptography. He is a senior member of IEEE.

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