诺玛
电信线路
计算机科学
资源配置
强化学习
数学优化
最优化问题
资源管理(计算)
无线
增强学习
传输(电信)
分布式计算
计算机网络
人工智能
算法
数学
电信
作者
Xinshui Wang,Ke Meng,Xu Wang,Zhibin Liu,Yuefeng Ma
出处
期刊:Energies
[MDPI AG]
日期:2023-03-24
卷期号:16 (7): 2984-2984
被引量:1
摘要
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.
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