The last couple of years have seen an emergence of physics-inspired computing for maximum likelihood MIMO detection. These methods involve transforming the MIMO detection problem into an Ising minimization problem, which can then be solved on an Ising Machine. Recent works have shown promising projections for MIMO wireless detection using Quantum Annealing optimizers and Coherent Ising Machines. While these methods perform very well for BPSK and 4-QAM, they struggle to provide good BER for 16-QAM and higher modulations. In this paper, we explore an enhanced CIM model, and propose a novel Ising formulation, which together are shown to be the first Ising solver that provides significant gains in the BER performance of large and massive MIMO systems, like 16×16 and 16×32, and sustain its performance gain even at 256-QAM modulation. We further perform a spectral efficiency analysis and show that, for a 16×16 MIMO with Adaptive Modulation and Coding, our method can provide substantial throughput gains over MMSE, achieving 2× throughput for SNR ≤25 dB, and up to 1.5× throughput for SNR ≥30 dB.
@article{arxiv.2205.05020,
title = {A Finite-Range Search Formulation of Maximum Likelihood MIMO Detection for Coherent Ising Machines},
author = {Abhishek Kumar Singh and Davide Venturelli and Kyle Jamieson},
journal= {arXiv preprint arXiv:2205.05020},
year = {2023}
}