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arxiv: 2504.09820 · v3 · submitted 2025-04-14 · 📡 eess.SP · cs.IT· math.IT

Finite-Precision Conjugate Gradient Method for Massive MIMO Detection

classification 📡 eess.SP cs.ITmath.IT
keywords detectionfinite-precisioncomputationalanalysiscomplexityconjugategradientmassive
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Low-rank Preconditioning in Beamspace Domain For Massive MU-MIMO Long-Term Beamforming

    eess.SP 2026-05 unverdicted novelty 5.0

    Low-rank preconditioner from top eigenpairs of the covariance matrix via randomized EVD with QRC, applied in beamspace, reduces CG iterations by 2-3x for long-term beamforming while matching exact inversion SINR.

  2. Interference Suppression for Massive MU-MIMO Long-Term Beamforming with Matrix Inversion Approximation

    eess.SP 2026-04 unverdicted novelty 5.0

    Subspace nulling on long-term statistics preconditions the LTBF covariance matrix to reduce CG iterations and improve numerical stability in massive MU-MIMO.