Compressive Sensing based Channel Estimation for Orthogonal Time Frequency Space (OTFS)

Abstract

High speed mobility is a challenging case in wireless communication where orthogonal frequency division multiplexing (OFDM) performance degrades due tohigh Doppler effect. A new modulation scheme orthogonal time frequency space (OTFS) that operates in the delay-Doppler (DD) domain is proposed in the literature. Considering the sparse nature of the delay-Doppler (DD) channel, we model the estimation of the channel as a sparse signal recovery problem. To solve this problem, we use compressed sensing (CS) based estimation techniques. We apply orthogonal matching pursuit (OMP), generalized OMP (gOMP), orthogonal least square (OLS) and generalized OLS (gOLS) based algorithms for DD channel estimation. We compare the performance of the proposed CS-based estimation schemes.We analyse the performance of the CS techniques for a grid pattern which has pilot symbols embedded in the data frame. We further extended the OTFS system for multi user case and analyse the performance of the CS-based channel estimation schemes.