IRS-Assisted OTFS: Beamforming Design and Signal Detection

TL;DR

This paper proposes a novel IRS-assisted OTFS system with joint beamforming and signal detection techniques, significantly improving capacity and BER performance in wireless communications.

Contribution

It introduces an AO-based precoding design and an ADMM-based signal detector for IRS-assisted OTFS, addressing the non-convex optimization challenges.

Findings

Enhanced system capacity demonstrated through simulations

Improved BER performance over conventional OTFS

Effective cascaded MIMO channel estimation using tensor models

Abstract

Intelligent reflecting surface (IRS) technology has become a crucial enabler for creating cost effective, innovative, and adaptable wireless communication environments. This study investigates an IRS-assisted orthogonal time frequency space (OTFS) modulation that facilitates communication between users and the base station (BS). The users attainable downlink rate can be boosted by collaboratively improving the reflection coefficient (RC) matrix at the IRS and beamforming matrix at the BS. Then, in the IRS-aided OTFS network, the problem of cooperative precoding at BS and IRS to improve the network throughput is framed. The precoding design problem is non-convex and highly complicated; an alternate optimization (AO) approach is proposed to solve this. Specifically, an approach based on strongest tap maximization (STM) and fractional programming is proposed. It solves RC matrix (at IRS) and beamforming matrix (at BS) alternatively. Moreover, an efficient signal detector for IRS-aided OTFS communication systems using the alternating direction method of multipliers (ADMM) is proposed. Finally, to estimate the cascaded MIMO channel, using a parallel factor tensor model that separates the IRS-User and BS-IRS MIMO channels, respectively is suggested. Simulation results show that the proposed method significantly enhances the system capacity and bit error rate (BER) performance compared to conventional OTFS.