Low-Complexity Equalization and Detection for OTFS-NOMA

TL;DR

This paper introduces a low-complexity iterative equalization and detection method for OTFS-NOMA, significantly improving symbol error rate performance over existing MMSE-SIC benchmarks in high-mobility scenarios.

Contribution

It proposes a novel iterative equalization and detection scheme combining LSQR-based equalization with a reliability zone detection and interference cancellation for OTFS-NOMA.

Findings

Proposed method outperforms MMSE-SIC in symbol error rate.

Reliability zone thresholds critically affect performance.

Iterative approach effectively mitigates multi-user interference.

Abstract

Orthogonal time frequency space (OTFS) modulation has recently emerged as a potential 6G candidate waveform which provides improved performance in high-mobility scenarios. In this paper we investigate the combination of OTFS with non-orthogonal multiple access (NOMA). Existing equalization and detection methods for OTFS-NOMA, such as minimum-mean-squared error with successive interference cancellation (MMSE-SIC), suffer from poor performance. Additionally, existing iterative methods for single-user OTFS based on low-complexity iterative least-squares solvers are not directly applicable to the NOMA scenario due to the presence of multi-user interference (MUI). Motivated by this, in this paper we propose a low-complexity method for equalization and detection for OTFS-NOMA. Our proposed method uses an iterative process where in each iteration, an equalizer based on the least-squares with QR factorization (LSQR) algorithm is followed by a novel reliability zone (RZ) detection scheme which estimates the reliable symbols of the users and then uses interference cancellation to remove MUI. We present numerical results which demonstrate the superiority of our proposed method, in terms of symbol error rate (SER), to the existing MMSE-SIC benchmark scheme. Additionally, we present results which illustrate that a judicious choice of RZ thresholds is important for optimizing the performance of the proposed algorithm.