An SISO-OTFS Channel Parameter Learning Scheme in Time-Frequency Domain

Abstract

Orthogonal Time Frequency Space (OTFS) modulation is a recently proposed modulation pattern aiming to overcome problems in high mobility scenarios. Parameter learning, including both the Delay-Doppler(DD) domain and the Time-Frequency(TF) domain learning, is one of the most important research direction of OTFS. Rough parameter learning in the TF domain is preferred for its lower cost. In this paper, we proposed a Time-Frequency domain parameter learning scheme in Single-Input Single-Output OTFS (SISO-OTFS) scene. Firstly, the 2D uplink(UL) channel model and the received signal model are studied, where the problem is converted into a sparse estimation problem. Secondly, Fast Fourier Transform(FFT) is utilized to precisely estimate the doppler shift and the channel gain of each path. Thirdly, rough and accurate searches are applied to get a precise estimation of the doppler shift and time delay. With the proposed scheme, the estimation complexity is reduced, and the prior knowledge for more precise DD domain pilot design and channel estimation could be acquired.