中图分类号： TN925 文献标识码： A DOI：10.16157/j.issn.0258-7998.222520 中文引用格式： 曾相誌，申滨，阳建. 基于SDNSR-Net深度网络的大规模MIMO信号检测算法[J].电子技术应用，2022，48(11)：84-88. 英文引用格式： Zeng Xiangzhi，Shen Bin，Yang Jian. Signal detection based on SDNSR-Net deep network for massive MIMO systems[J]. Application of Electronic Technique，2022，48(11)：84-88.
Signal detection based on SDNSR-Net deep network for massive MIMO systems
Zeng Xiangzhi，Shen Bin，Yang Jian
School of Communication and Information Engineering，Chongqing University of Posts and Telecommunications， Chongqing 400065，China
Abstract： Massive multiple-input multiple-output(MIMO) systems can effectively improve the spectrum efficiency. When the antenna scale gradually tends to infinity, the minimum mean square error(MMSE) detection algorithm can achieve near-optimal detection performance. However, due to the matrix inversion required in the algorithm, which brings extremely high computational complexity, it is difficult to implement in a massive MIMO system. The Richardson algorithm can achieve the detection performance of the MMSE algorithm in an iterative form without matrix inversion, but the algorithm is greatly affected by its relaxation parameters. In the Richardson algorithm combined with the steepest gradient descent algorithm (SDNSR), the error of the relaxation parameter can be compensated by the gradient descent algorithm, but the computational complexity is increased. This paper firstly uses the idea of deep expansion to map the iterative process of SDNSR to a deep detection network (SDNSR-Net); then, by modifying the network structure and adding trainable parameters，the computational complexity is reduced and the detection accuracy is improved. The experimental results show that SDNSR-Net is superior to other typical detection algorithms in the case of different signal-to-noise ratios and antenna configurations in the uplink massive MIMO system and can be used as an effective detection scheme in practice.
Key words : massive MIMO system；signal detection；modern driven；deep learning
大规模MIMO系统中存在信道硬化现象，即由信道矩阵生成的Gram矩阵的对角项远大于非对角项。在该情况下最小均方误差(Minimum Mean Square Error，MMSE)检测算法已证明可以达到次优的检测性能。然而该算法中存在矩阵求逆运算，因此难以适用于大规模MIMO系统。
为降低线性检测算法的计算复杂度，出现了Richardson迭代、Jacobi迭代和逐次超松弛(Successive Over Relaxation，SOR)迭代等迭代检测算法。然而，在大规模MIMO系统中，随着用户增加，该类算法的检测性能退化严重。