基于ROAMP-Net的大规模MIMO系统智能信号检测方法OACSTPCD

The signal detection in massive multiple-input multiple-output(MIMO)systems usually confronts the challenges of high computation complexity and low detection accuracy.Artificial intelligence technologies have been widely applied to improve the performance of signal detection.OAMP-Net is a signal detection algorithm based on deep learning,and its com-prehensive performance is relatively better than other typical signal detection algorithms.Inspired by the ideas of OAMP-Net,we propose a new intelligent signal detection model,i.e.ROAMP-Net,by introducing residual structure.In ROAMP-Net,the iteration of orthogonal approximate message passing(OAMP)is extended to a deep learning network.Meanwhile,to prevent the performance degradation of deep network with the increase of network layers,the model introduces residual structure to correct the linear and non-linear signal estimation layer by layer,so that the estimation errors would not be for-warded and accumulated.Consequently,high accuracy of signal detection can be expected.Simulation experimental tests suggest that ROAMP-Net outperforms many benchmarks on the accuracy of signal detection under different modulation methods and antenna arrays.