1
Resource Allocation for Energy Harvesting-Powered
D2D Communications Underlaying Cellular
Networks
Haichao Wang
∗
, Guoru Ding
∗†
, Jinlong Wang
∗
, Le Wang
∗
, Theodoros A. Tsiftsis
‡
, and Prabhat K. Sharma
§
∗
College of Communications Engineering, PLA University of Science and Technology, Nanjing, China.
†
National Mobile Communications Research Laboratory, Southeast University, Nanjing, China.
‡
School of Engineering, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana, Kazakhstan.
§
Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology,
Nagpur, India.
Email: whcwl0919@sina.com, dr.guoru.ding@ieee.org, wjl543@sina.com, wlwhc0919@sina.com,
theodoros.tsiftsis@nu.edu.kz, prabhatmadhavec1@gmail.com
Abstract—Device-to-device communication and energy har-
vesting are both key technologies to improve spectrum and energy
efficiency. In this paper, we investigate the resource allocation
problem for the energy harvesting-powered D2D communication
underlaying cellular networks, where D2D pairs firstly harvest
energy and then transmit information signals. The goal is to max-
imize the sum throughput via joint time scheduling and power
control while satisfying the SINR requirement of cellular user
and taking into account the energy constraint. The formulated
non-convex problem is transformed into a nonlinear fractional
programming problem with a tactful reformulation. Coupled
with D.C. (difference of two convex functions) programming, a
near optimal solution of the non-convex problem can be obtained
by iteratively solving a sequence of convex problems. Then, a
first-order algorithm is employed to solve these convex problems.
Numerical simulations are conducted to validate the effectiveness
of the proposed algorithm and evaluate the system throughput
performance.
Index Terms—D.C. programming; device-to-device (D2D); en-
ergy harvesting; fractional programming; resource allocation.
I. INTRODUCTION
Device-to-device (D2D) communication underlaying cellu-
lar networks is a promising technology to improve spectrum
efficiency by enabling nearby end users to communicate direct-
ly [1]–[4]. Hence, it is not a surprise that D2D communication
has captured much research attention. However, the system
performance is confined by the limited battery lifetime, which
greatly advances the development of radio frequency (RF)
energy harvesting technology [5]. Compared with traditional
networks, users in energy harvesting networks have the ability
of information transmission, as well as energy harvesting,
making it possible that the lifetime is prolonged and the system
performance is thus improved.
Our work is related to the studies on the resource allo-
cation method for D2D communication underlaying cellular
networks, in which the spectrum efficiency advantage cannot
gloss over the new challenges brought by sharing the same
frequency spectrum with the cellular user (CU), such as the
interference between the CU and D2D pairs. Therefore, a
number of studies investigate the resource sharing schemes for
D2D communication underlaying cellular networks [6]–[9].
Specifically, in [6], a three-steps resource allocation framework
was presented to maximize the sum throughput, including
QoS-based admission control, optimal power control and
maximum weighted matching. Simulation results validate the
superiority on access rate and throughput performance. In [7],
the authors proposed an alternating optimization method to
solve the resource allocation problem, which can significantly
improve the system throughput, however, with high complex-
ity. In [8], a frequency band sharing protocol was designed to
allocate spectrum resource where multiple D2D pairs can share
subchannels with multiple CUs. In [9], the authors proposed a
distributed pricing-based interference coordination framework
to solve the resource allocation problem.
Our work is also related to the studies on the resource
allocation method for RF energy harvesting networks, which
is greatly advocated by the dual use of RF signals. The
existing resource allocation methods for RF energy harvesting
networks mostly focus on the throughput optimiztion [10]–
[14] and energy efficiency optimization [15], [16]. Specifically,
in [10], the authors investigated the channel selection problem
by using Markov decision process and got an optimal policy
based on the system state to maximize the throughput. In
[11], the case of imperfect channel state information (CSI)
was investigated and an online learning algorithm to update
the channel selection strategy was provided. A harvest-then-
transmit model was proposed in [12], where the users first har-
vest energy and then transmit information signals by harvested
energy. The case of multi-antenna access points was extended
in [13]. The authors in [14] proposed a power allocation
algorithm with the goal to maximize the throughput under the
constraints of power consumption.
Beyond all doubt, combining D2D communication under-
laying cellular networks and energy harvesting technology
can provide high spectrum and energy efficiency. However,
there are very few studies on the energy harvesting-powered
D2D communication underlaying cellular networks [17], [18].
IEEE ICC 2017 Mobile and Wireless Networking
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