Data-driven deep learning for OTFS detection

Abstract

Recently, orthogonal time frequency space (OTFS) was presented to alleviate severe Doppler effects in high mobility scenarios. Most of the current OTFS detection schemes rely on perfect channel state information (CSI). However, in real-life systems, the parameters of channels will constantly change, which are often difficult to capture and describe. In this paper, we summarize the existing research on OTFS detection based on data-driven deep learning (DL) and propose three new network structures. The presented three networks include a residual network (ResNet), a dense network (DenseNet), and a residual dense network (RDN) for OTFS detection. The detection schemes based on data-driven paradigms do not require a model that is easy to handle mathematically. Meanwhile, compared with the existing fully connected-deep neural network (FC-DNN) and standard convolutional neural network (CNN), these three new networks can alleviate the problems of gradient explosion and gradient disappearance. Through simulation, it is proved that RDN has the best performance among the three proposed schemes due to the combination of shallow and deep features. RDN can solve the issue of performance loss caused by the traditional network not fully utilizing all the hierarchical information.