A Survey on Low Complexity Detectors for OTFS Systems

The newly emerging orthogonal time frequency space(OTFS)modulation can ob⁃tain delay-Doppler diversity gain to significantly improve the system performance in high mobility wireless communication scenarios such as vehicle-to-everything(V2X),high-speed railway and unmanned aerial vehicles(UAV),by employing inverse symplectic finite Fouri⁃er transform(ISFFT)and symplectic finite Fourier transform(SFFT).However,OTFS modu⁃lation will dramatically increase system complexity,especially at the receiver side.Thus,de⁃signing low complexity OTFS receiver is a key issue for OTFS modulation to be adopted by new-generation wireless communication systems.In this paper,we review low complexity OTFS detectors and provide some insights on future researches.We firstly present the OTFS system model and basic principles,followed by an overview of OTFS detector structures,classifications and comparative discussion.We also survey the principles of OTFS detection algorithms.Furthermore,we discuss the design of hybrid OTFS and orthogonal frequency di⁃vision multiplexing(OFDM)detectors in single user and multi-user multi-waveform commu⁃nication systems.Finally,we address the main challenges in designing low complexity OT⁃FS detectors and identify some future research directions.

A low-complexity AMP detection algorithm with deep neural network for massive mimo systems

Signal detection plays an essential role in massive Multiple-Input Multiple-Output(MIMO)systems.However,existing detection methods have not yet made a good tradeoff between Bit Error Rate(BER)and computational complexity,resulting in slow convergence or high complexity.To address this issue,a low-complexity Approximate Message Passing(AMP)detection algorithm with Deep Neural Network(DNN)(denoted as AMP-DNN)is investigated in this paper.Firstly,an efficient AMP detection algorithm is derived by scalarizing the simplification of Belief Propagation(BP)algorithm.Secondly,by unfolding the obtained AMP detection algorithm,a DNN is specifically designed for the optimal performance gain.For the proposed AMP-DNN,the number of trainable parameters is only related to that of layers,regardless of modulation scheme,antenna number and matrix calculation,thus facilitating fast and stable training of the network.In addition,the AMP-DNN can detect different channels under the same distribution with only one training.The superior performance of the AMP-DNN is also verified by theoretical analysis and experiments.It is found that the proposed algorithm enables the reduction of BER without signal prior information,especially in the spatially correlated channel,and has a lower computational complexity compared with existing state-of-the-art methods.