Expectation Propagation Aided Model Driven Learning for OTFS Signal Detection
Shuo Li, Chao Ding, Lixia Xiao, Xufan Zhang, Guanghua Liu, Tao Jiang
Abstract
Orthogonal time frequency space (OTFS) modulation, which modulates constellation symbols in the delay-Doppler (DD) domain, is capable of outperforming conventional multi-carrier schemes in the context of high mobility communication scenarios. However, due to its special structure, it poses serious challenge to its signal detection, especially in a rich-scattering environment. To address this issue, in this paper, an expectation propagation (EP) aided model-driven deep learning framework is proposed for OTFS signal detection. Specifically, each iteration of the EP algorithm is unfolded into a layer-wise network and trainable parameters are added to this network to accelerate the convergence of the algorithm, as well as improve the detection performance. Simulation results show that the proposed EP aided model-driven network is capable of providing significant performance gain over conventional EP counterparts.