Dynamic User Resource Allocation for Downlink Multicarrier NOMA with an Actor–Critic Method

Xinshui Wang (), Ke Meng, Xu Wang, Zhibin Liu and Yuefeng Ma
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Xinshui Wang: School of Computer Science, Qufu Normal University, Rizhao 276826, China
Ke Meng: School of Computer Science, Qufu Normal University, Rizhao 276826, China
Xu Wang: School of Computer Science, Qufu Normal University, Rizhao 276826, China
Zhibin Liu: School of Computer Science, Qufu Normal University, Rizhao 276826, China
Yuefeng Ma: School of Computer Science, Qufu Normal University, Rizhao 276826, China

Energies, 2023, vol. 16, issue 7, 1-15

Abstract: Future wireless communication systems require higher performance requirements. Based on this, we study the combinatorial optimization problem of power allocation and dynamic user pairing in a downlink multicarrier non-orthogonal multiple-access (NOMA) system scenario, aiming at maximizing the user sum rate of the overall system. Due to the complex coupling of variables, it is difficult and time-consuming to obtain an optimal solution, making engineering impractical. To circumvent the difficulties and obtain a sub-optimal solution, we decompose this optimization problem into two sub-problems. First, a closed-form expression for the optimal power allocation scheme is obtained for a given subchannel allocation. Then, we provide the optimal user-pairing scheme using the actor–critic (AC) algorithm. As a promising approach to solving the exhaustive problem, deep-reinforcement learning (DRL) possesses higher learning ability and better self-adaptive capability than traditional optimization methods. Simulation results have demonstrated that our method has significant advantages over traditional methods and other deep-learning algorithms, and effectively improves the communication performance of NOMA transmission to some extent.

Keywords: NOMA; deep-reinforcement learning; actor–critic; power allocation; user pairing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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