Complex-Valued Neural Networks for Ultra-Reliable Massive MIMO

TL;DR

This paper introduces a neural network-based decoding scheme for complex-valued MIMO systems using QOSTBC and SVD, significantly enhancing reliability and spectral efficiency for next-generation wireless networks.

Contribution

It proposes a novel neural network decoding method for QOSTBC in MIMO systems, improving reliability and reducing computational complexity in ultra-reliable communications.

Findings

Enhanced spectral efficiency with QOSTBC

Improved robustness over traditional methods

Neural decoding reduces complexity

Abstract

In the evolving landscape of 5G and 6G networks, the demands extend beyond high data rates, ultra-low latency, and extensive coverage, increasingly emphasizing the need for reliability. This paper proposes an ultra-reliable multiple-input multiple-output (MIMO) scheme utilizing quasi-orthogonal space-time block coding (QOSTBC) combined with singular value decomposition (SVD) for channel state information (CSI) correction, significantly improving performance over QOSTBC and traditional orthogonal STBC (OSTBC) when analyzing spectral efficiency. Although QOSTBC enhances spectral efficiency, it also increases computational complexity at the maximum likelihood (ML) decoder. To address this, a neural network-based decoding scheme using phase-transmittance radial basis function (PT-RBF) architecture is also introduced to manage QOSTBC's complexity. Simulation results demonstrate improved system robustness and performance, making this approach a potential candidate for ultra-reliable communication in next-generation networks.