Symbol Detection for Coarsely Quantized OTFS

TL;DR

This paper addresses the challenge of symbol detection in coarsely quantized OTFS systems by developing a low-complexity algorithm that maintains detection performance while significantly reducing computational complexity.

Contribution

We propose a novel low-complexity algorithm that integrates quick inversion of quasi-banded matrices into GEC-SR for efficient symbol detection in quantized OTFS systems.

Findings

The proposed algorithm reduces complexity from cubic to linear order.

It effectively mitigates the loss caused by coarse quantization.

Performance remains comparable to high-complexity methods.

Abstract

This paper explicitly models a coarse and noisy quantization in a communication system empowered by orthogonal time frequency space (OTFS) for cost and power efficiency. We first point out, with coarse quantization, the effective channel is imbalanced and thus no longer able to circularly shift the transmitted symbols along the delay-Doppler domain. Meanwhile, the effective channel is non-isotropic, which imposes a significant loss to symbol detection algorithms like the original approximate message passing (AMP). Although the algorithm of generalized expectation consistent for signal recovery (GEC-SR) can mitigate this loss, the complexity in computation is prohibitively high, mainly due to an dramatic increase in the matrix size of OTFS. In this context, we propose a low-complexity algorithm that incorporates into the GEC-SR a quick inversion of quasi-banded matrices, reducing the complexity from a cubic order to a linear order while keeping the performance at the same level.