基于SDNSR-Net深度网络的大规模MIMO信号检测算法

Signal detection based on SDNSR-Net deep network for massive MIMO systems

大规模多输入多输出(MIMO)系统能有效地提高频谱效率,当天线规模渐进趋向于无穷时,最小均方误差(MMSE)检测算法能达到接近最优的检测性能。然而由于算法中存在矩阵求逆的步骤,带来极高的计算复杂度,在大规模MIMO系统中难以实现。理查森(Richardson)算法能够在不对矩阵求逆的情况下,以迭代的形式达到MMSE算法的检测性能,但该算法受其松弛参数影响较大。在结合最陡梯度下降算法的Richardson算法(SDNSR)中,松弛参数的误差可由梯度下降算法弥补,却提高了计算复杂度。首先通过深度展开的思想,将SDNSR的迭代过程映射为深度检测网络(SDNSR-Net);然后,通过修改网络结构及添加可训练参数来降低计算复杂度并提高检测精度。实验结果表明,在上行链路大规模MIMO系统中不同信噪比和天线配置的情况下,SDNSR-Net都优于其他典型的检测算法,可作为实际中有效的待选检测方案。

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.