数据采集与处理2025，Vol.40Issue(6)：1477-1489,13.DOI:10.16337/j.1004-9037.2025.06.008
基于指针网络深度强化学习NOMA用户配对和功率分配方案
NOMA User Pairing and Power Allocation Scheme of Deep Reinforcement Learning Based on Pointer Network
摘要
Abstract
To solve the fast pairing and power allocation problem of non-orthogonal multiple access(NOMA)under imperfect serial interference cancellation(SIC)conditions,a deep reinforcement learning-based user pairing and power optimization scheme is proposed.First,this paper considers the scenario of imperfect SIC for multiuser NOMA,and constructs an optimization problem to maximize the system reachable communication rate with user pairing and user transmit power allocation factor as optimization variables.The condition of user pairing using NOMA under the imperfect SIC condition is analyzed,and the user power allocation for the maximum reachable rate under this condition is introduced.Second,the user pairing problem is treated as a combinatorial optimization problem,and a novel user pairing scheme is designed based on the real-time requirement using an improved pointer network.Simulation results show that this scheme can effectively improve the reachable rate of the NOMA system to 99.8%of that of the optimal exhaustive search algorithm.It achieves real-time performance and adapts to the dynamic change of the number of users.关键词
非正交多址接入/用户配对/功率分配/不完美串行干扰消除/深度强化学习Key words
non-orthogonal multiple access(NOMA)/user pairing/power allocation/imperfect serial interference cancellation(SIC)/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
李国鑫,甘麒,陈瑾,焦雨涛,王海超,贺兴..基于指针网络深度强化学习NOMA用户配对和功率分配方案[J].数据采集与处理,2025,40(6):1477-1489,13.基金项目
国家自然科学基金(62471489,62271501) （62471489,62271501）
国家资助博士后研究人员计划(GZB20240996) （GZB20240996）
江苏省自然科学基金(BK20240200) （BK20240200）
江苏省前沿引领技术基础研究重大项目(BK20212001). （BK20212001）
