Finite-Precision Conjugate Gradient Method for Massive MIMO Detection

2025-04-14

AI Summary

In large-scale MIMO detection, conventional conjugate gradient (CG) methods suffer from high computational overhead and slow convergence—especially under highly correlated channels, where performance degrades significantly. To address this, this paper presents the first systematic finite-precision analysis of CG, explicitly modeling the trade-off among numerical precision, convergence rate, and computational complexity. We propose a finite-precision CG (FP-CG) detector and its block Jacobi preconditioned variant (FP-BJ-CG). Innovatively, we design an adaptive low-precision strategy and theoretically prove that FP-CG retains linear convergence with bounded error propagation. FP-BJ-CG reduces iteration counts by up to 40%. Simulation results demonstrate that, under correlated channel conditions, the proposed methods achieve 2–3 dB BER gains over state-of-the-art detectors while reducing computational complexity by over 50%, thereby significantly enhancing hardware efficiency and energy efficiency.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Non-convex Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

The implementation of the conjugate gradient (CG) method for massive MIMO detection is computationally challenging, especially for a large number of users and correlated channels. In this paper, we propose a low computational complexity CG detection from a finite-precision perspective. First, we develop a finite-precision CG (FP-CG) detection to mitigate the computational bottleneck of each CG iteration and provide the attainable accuracy, convergence, and computational complexity analysis to reveal the impact of finite-precision arithmetic. A practical heuristic is presented to select suitable precisions. Then, to further reduce the number of iterations, we propose a joint finite-precision and block-Jacobi preconditioned CG (FP-BJ-CG) detection. The corresponding performance analysis is also provided. Finally, simulation results validate the theoretical insights and demonstrate the superiority of the proposed detection.

Problem

• Reducing computational complexity in massive MIMO detection
• Mitigating finite-precision arithmetic impact on CG method
• Improving convergence with preconditioned CG for correlated channels

Innovation

• Finite-precision CG for MIMO detection
• Block-Jacobi preconditioned CG method
• Heuristic precision selection for efficiency