一种应用于协作MIMO-NOMA系统的符号检测算法
A Symbol Detection Algorithm for Cooperative MIMO-NOMA Systems
谢文武 1李攀 1肖健 2王骥 2杨亮3
作者信息
- 1. 湖南理工学院 信息科学与工程学院,岳阳414006
- 2. 华中师范大学 物理科学与技术学院,武汉430079
- 3. 湖南大学 信息科学与工程学院,长沙410082
- 折叠
摘要
受发射端的功率分配与叠加编码的影响,基于单任务神经网络的功率域非正交多址接入(NOMA)符号检测算法无法兼容不同用户的符号检测任务.针对用户辅助的协作多输入多输出(MIMO)-NOMA通信系统,设计基于多任务神经网络的符号检测算法,通过学习协作MIMO-NOMA系统中信号的深层共享特征,实现不同用户的联合符号检测.由于协作通信中不同用户接收信号的数据分布不同,并且存在数据孤岛问题,而机器学习模型要求训练数据和测试数据均独立采样于同一数据分布,因此提出多任务联邦学习框架来解决这一问题.实验结果表明,随着信噪比的提高,所提出的符号检测算法较传统符号检测算法展现出更好的性能.
Abstract
Influenced by the power allocation and superposition coding at the transmitter side,the power-domain non-orthogonal multiple access ( NOMA) symbol detection algorithm based on a single-task neural network is not compatible with the symbol detection task for different users.A symbol detection algorithm based on multi-task neural network is designed for user-assisted cooperative multiple-input multiple-output (MIMO)-NOMA communication system,which can learn the deep shared features of data and detect symbols of different users simultaneously.In cooperative communication,the signal data received by different users are distributed differently,and there is a problem of data island.However,the training data and the test data are required by the machine learning model to be independently and equally distributed.Therefore,the multi-task federal learning framework isproposed to address this problem.The experimental results show that with the improvement of signal-noise-ratio ( SNR),the proposed symbol detection algorithm has better performance than the traditional symbol detection algorithm.
关键词
协作非正交多址接入/符号检测/多任务联邦学习Key words
cooperative non-orthogonal multiple access/symbol detection/multi-task federated learning引用本文复制引用
基金项目
国家自然科学基金项目(62101205)
湖南省自然科学基金项目(2023JJ50045)
湖北省重点研发计划项目(2023BAB061)
出版年
2024
NETL
NSTL
万方数据