基于SIC的RL用于大规模的MIMO NOMA信号检测,用于不同的调制方案在不同的通道条件下
Arun Kumar1, Aziz Nanthaamornphong2, Mohammed H Alsharif3
1Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Rangpo, India.
Scientific reports
|July 9, 2025
概括
本研究介绍了用于大型多输入多输出非直角多接入系统 (M-MIMO-NOMA) 的连续干扰取消与强化学习 (SIC-RL) 探测器. 与传统方法相比,SIC-RL显著提高了光谱效率,并降低了比特错误率.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 大规模多输入多输出非直角多接入 (M-MIMO-NOMA) 系统在信号检测方面面临挑战,原因是干扰和光谱效率要求.
- 有效的检测对于在各种通道条件和调制方案下优化性能至关重要.
研究的目的:
- 为了研究连续干扰取消与强化学习 (SIC-RL) 探测器的性能.
- 为了比较SIC-RL与传统探测器,如MMSE,MLD,AMP,GS,CG和ZFE.
- 评估性能指标,包括比特错误率 (BER),功率光谱密度 (PSD) 和计算复杂性.
主要方法:
- 这项研究采用了使用16-QAM,64-QAM和256-QAM调制方案在雷利淡化通道中的模拟.
- 在不同的通道条件下分析了性能,包括10%的误差.
- 对比SIC-RL与最小平均平方误差 (MMSE),最大概率检测 (MLD),近似消息传递 (AMP),高斯-赛德尔 (GS),结合梯度 (CG) 和零强迫等效器 (ZFE).
主要成果:
- 与传统探测器相比,SIC-RL在BER,PSD和计算复杂性方面表现出卓越的性能.
- 在10−3的BER下,SIC-RL在不同的QAM方案中实现了显著的SNR增长 (例如,256-QAM的6.6dB).
- 在SIC-RL的天线数下,光谱泄漏率降低了35%,复杂度增长接近对数,超过了MLD的指数复杂度.
结论:
- SIC-RL是大规模MIMO信号检测的最佳解决方案,在准确性和效率之间提供了卓越的权衡.
- 它为下一代MIMO-NOMA系统提供了BER,PSD和计算复杂性的显著改进.
- SIC-RL的可扩展性和性能使其成为未来无线通信系统的有希望的探测器.
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