Distributed Penalty Nesterov Gradient Algorithm For Extremely Large-Scale MIMO Systems

doi:10.1109/TSP.2026.3660267

Distributed Penalty Nesterov Gradient Algorithm For Extremely Large-Scale MIMO Systems

2026-02-03

0

PRE

AI

DOI：10.1109/TSP.2026.3660267

Original

Original request for help

Save

Abstract

En 中文

In this paper, we propose the distributed penalty Nesterov gradient (DPNG) detection algorithm for uplink extremely large-scale multiple-input multiple-output (XL-MIMO) systems, to address the performance limitations of conventional linear detectors. Instead of directly solving the maximum likelihood (ML) detection problem, we introduce a convex relaxation by incorporating a tailored penalty function that enforces alignment between the estimates and the constellation set. By integrating the diffusion strategy into reformulated detection problem, the DPNG algorithm enables efficient distributed computation. According to theoretical analysis, we prove this reformulated detection problem to be strongly convex and Lipschitz continuous. Leveraging these properties, the optimal solution of the reformulated problem achieves better performance than traditional linear detection. Furthermore, theoretical result demonstrates that the proposed DPNG algorithm efficiently finds this solution with a sublinear convergence rate, precisely quantifying the trade-off between detection performance and computational complexity. Extensive simulations confirm that DPNG achieves notable BER improvements with lower complexity and bandwidth, thereby providing practical advantages over prior methods in distributed XL-MIMO detection.

Keywords:

Extremely large-scale MIMO

distributed MIMO detection

decentralized baseband processing

diffusion strategy

penalty function

Distributed Penalty Nesterov Gradient Algorithm For Extremely Large-Scale MIMO Systems

Abstract

Journal

Organization