基于干扰消除辅助稀疏连接神经网络的大规模MIMO信号检测

Massive MIMO Signal Detection Based on Interference Cancellation Assisted Sparsely Connected Neural Network

近年来,深度学习成为无线通信领域的关键技术之一。在基于深度学习的一系列MIMO信号检测算法中,大多未充分考虑相邻天线之间的干扰消除问题,无法彻底消除多用户干扰对误码率性能的影响。为此,该文提出一种将深度学习与串行干扰消除(SIC)算法进行结合的方法用于大规模MIMO系统上行链路信号检测。首先,通过优化传统的检测网络(DetNet)及改进ScNet检测算法,该文提出一种基于深度神经网络(DNN)的检测算法,称为ImpScNet。在此基础上,进一步将SIC思想应用到深度学习框架结构设计中,提出一种基于深度学习的大规模MIMO多用户SIC检测算法,称为ImpScNet-SIC。此算法在每个检测层上分为两级,其中,第1级由该文提出的ImpScNet算法提供初始解,再将初始解解调至相应的星座点上作为SIC的输入,由此构成该算法的第2级。此外,在SIC中也使用了ImpScNet算法估计传输符号,以便获得最优性能。仿真结果表明,与已有的各种典型代表算法相比,该文所提ImpScNet-SIC检测算法特别适合大规模MIMO信号检测,具有收敛速度快、收敛稳定及复杂度相对较低的优势,并且在10–3误码率上有至少0.5 dB以上的增益。

In recent years, deep learning has become one of the key technologies in the field of wireless communication. In a series of MIMO signal detection algorithms based on deep learning, most of them do not fully consider the interference cancellation problem between adjacent antennas, hence the impact of multi-user interference on the bit error rate performance can not be completely eliminated. To this end, a method that combines deep learning and Successive Interference Cancellation(SIC) algorithms for uplink signal detection in a massive MIMO system is propesed. Firstly, by optimizing the traditional Detection Network(DetNet) and improving the ScNet(Sparsely connected neural Network), a detection algorithm based on the Deep Neural Network(DNN), called Improved ScNet(ImpScNet), is proposed. On this basis, the SIC is applied to the design of the deep learning framework structure, and a massive MIMO multi-user SIC detection algorithm based on deep learning is proposed, which is called ImpScNet-SIC. This algorithm is divided into two stages on each detection layer. The first stage is provided by the ImpScNet algorithm proposed in this paper to provide the initial solution, and then the initial solution is demodulated to the corresponding constellation point as the input of the SIC, which constitutes the second stage. In addition, the ImpScNet algorithm is also used in SIC to estimate the transmitted symbols in order to obtain the best performance. Simulation results show that,compared with various typical representative algorithms, the ImpScNet-SIC detection algorithm proposed in this paper is particularly suitable for the massive MIMO signal detection. It has the advantages of fast convergence speed, stable convergence and relatively low complexity. And there is at least 0.5 dB gain in 10–3bit error rate.