Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation

TL;DR

This paper introduces a Bayesian neural network-based detector for OTFS modulation in high-mobility wireless systems, significantly improving detection performance in complex scattering environments.

Contribution

It proposes a novel BPICNet OTFS detector combining neural networks, Bayesian inference, and interference cancellation, enhancing detection accuracy over existing methods.

Findings

Outperforms state-of-the-art OTFS detectors in simulations

Effective in rich scattering environments with many reflectors

Demonstrates robustness in high-mobility scenarios

Abstract

The orthogonal time-frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications. The existing low complexity OTFS detectors exhibit poor performance in rich scattering environments where there are a large number of moving reflectors that reflect the transmitted signal towards the receiver. In this paper, we propose an OTFS detector, referred to as the BPICNet OTFS detector that integrates NN, Bayesian inference, and parallel interference cancellation concepts. Simulation results show that the proposed OTFS detector significantly outperforms the state-of-the-art.