Energy Efficiency Maximization via Joint Sub-Carrier Assignment and Power Control for OFDMA Full Duplex Networks

Abstract

In this paper, we develop an energy efficient resource allocation scheme for orthogonal frequency division multiple access (OFDMA) networks with in-band full-duplex (IBFD) communication between the base station and user equipments (UEs) considering a realistic self-interference (SI) model. Our primary aim is to maximize the system energy efficiency (EE) through a joint power control and sub-carrier assignment in both the downlink (DL) and uplink (UL), where the quality of service requirements of the UEs in DL and UL are guaranteed. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle this difficulty, we first use fractional programming to transform the fractional objective function to the subtractive form. Then, by employing Dinkelbach method, we propose an iterative algorithm in which an inner problem is solved in each iteration. Applying majorization-minimization approximation, we make the inner problem convex. Also, by introducing a penalty function to handle integer sub-carrier assignment variables, we propose an iterative algorithm for addressing the inner problem. We show that our proposed algorithm converges to the locally optimal solution which is also demonstrated by our simulation results. In addition, simulation results show that by applying the IBFD capability in OFDMA networks with efficient SI cancellation techniques, our proposed resource allocation algorithm attains a 75% increase in the EE as compared to the half-duplex system. . His research interests include convex and non-convex optimization, resource allocation in wireless communication, Green communication, and mobile edge computing.

A Distributed Power Control Algorithm for Energy Efficiency Maximization in Wireless Cellular Networks

“…We observe that for all values of γ i , our proposed distributed algorithm performs like the centralized algorithm in [2]. In the algorithm in [2] executed in the BS, the BS has the global CSI of all UEs and obtains the transmit power control policy in a centralized manner. However, in our algorithm executed in the UEs, each UE controls its transmit power in a distributed manner using its CSI only.…”

“…From this figure, we see that our proposed scheme outperforms TPC and DTPC algorithms. More specifically, in the TPC algorithm, all UEs transmit in low power to Average Global EE (bits/Joule/Hz) Our Algorithm Algorithm in [2] Algorithm in [1] Fig . 3.…”

“…Additionally, simulation results demonstrate that our distributed algorithm which uses the local CSI performs like the centralized algorithm proposed in [2] in which the global CSI is assumed to be presented in the BS.…”

“…where, P C BS and P C i are the power consumed by the circuit of the BS and ith UE, respectively, and µ i is the power amplifier inefficiency of ith UE [2]. We define the GEE as the ratio of total throughput and total power consumption, given by:…”

“…In [1], three metrics for EE are defined: 1) Minimum-EE defined as the minimum EE of user equipments (UEs) in which the EE of a UE described by the ratio of the UE's throughput and its consumed power; 2) Sum-EE defined as the summation over the EE of UEs; 3) Global EE (GEE) defined as the ratio between the total throughput and the total power consumption of the system. As the GEE is the most commonly used metric for EE maximization problems in wireless cellular networks [2]- [5], we focus on it in this paper.…”