Performance of QUBO-Formulated MIMO Detection Under Hardware Precision Constraints

2026-05-12

AI Summary

This work addresses the significant performance degradation in MIMO detection caused by quantization noise when solving Quadratic Unconstrained Binary Optimization (QUBO) formulations on finite-precision hardware. The authors formulate MIMO detection as a QUBO problem and systematically analyze the impact of quantization on detection accuracy. They propose both homogeneous and heterogeneous quantization strategies leveraging either instantaneous channel state information or statistical channel properties. The study derives theoretical sufficient conditions on precision requirements to preserve the optimal solution and establishes practical quantization selection criteria tailored for hardware implementation. Experimental results demonstrate that the proposed heterogeneous quantization scheme achieves near full-precision bit error rate performance using substantially fewer bits across various MIMO configurations and modulation schemes up to 256-QAM.

Abstract

The evolution of multiple-input, multiple-output (MIMO) systems requires the efficient detection algorithms to overcome the exponential computational complexity of optimal maximum likelihood detection. Reformulating MIMO detection as a quadratic unconstrained binary optimization (QUBO) problem enables the use of highly parallel, physics-inspired, hardware-accelerated solvers and non-von Neumann architectures. However, embedding continuous-valued QUBO coefficients into hardware introduces quantization noise due to finite precision, which can severely degrade detection accuracy. This paper presents a rigorous analysis of the performance impact of finite-precision, hardware-accelerated QUBO solvers in MIMO detection. We analytically derive the probability distribution functions of the QUBO matrix entries and introduce novel homogeneous and heterogeneous quantization schemes based on either instantaneous channel state information or its statistical features. We further derive a sufficient condition on the precision required to maintain the optimal solution after quantization. Extensive numerical experiments, across various MIMO system sizes and modulation orders (up to 256-QAM), show that heterogeneous quantization matches the full-precision baseline bit error rate using significantly fewer bits than homogeneous approaches. We provide hardware-aware guidelines for selecting the optimal quantization strategy.

Problem

• MIMO detection
• QUBO
• finite-precision
• quantization noise
• hardware acceleration

Innovation

• QUBO
• MIMO detection
• finite-precision quantization
• heterogeneous quantization
• hardware-aware optimization