OTFS Channel Estimation And Data Detection Designs With Superimposed Pilots

TL;DR

This paper introduces superimposed pilot-based channel estimation and data detection methods for OTFS that improve spectral efficiency by avoiding dedicated pilot slots, using iterative detection and message passing algorithms.

Contribution

It proposes two novel SP-OTFS designs that enhance spectral efficiency and reduce complexity without reserving pilot slots, and derives a lower bound on SIR for optimal power allocation.

Findings

Proposed designs outperform existing OTFS methods in spectral efficiency.

Iterative detection improves performance at high SNR.

Analytical bounds guide optimal power allocation.

Abstract

This work proposes a superimposed pilot (SP)-based channel estimation and data detection framework for orthogonal time-frequency space (OTFS) scheme, wherein low-powered pilots are superimposed on to data symbols in the delay-Doppler domain. We propose two channel estimation and data detection designs for SP-OTFS systems which, unlike the existing OTFS designs, do not designate any slots for pilots, which improves their spectral efficiency (SE). The first SP design estimates channel by treating data as interference, which degrades its performance at high signal to noise ratio. The second SP design alleviates this problem by iterating between channel estimation and data detection. Both these designs detect data using message passing algorithm which exploits OTFS channel sparsity, and consequently has low computational complexity. We also derive a lower bound on the signal-to-interference-plus-noise ratio of the proposed designs, and maximize it by optimally allocating power between data and pilot symbols. We numerically validate the derived analytical results, and show that the proposed designs have superior SE than the two state-of-the-art OTFS channel estimation and data detection designs.