Massive multiple-input multiple-output (MIMO) technology has significantly enhanced spectral and power efficiency in cellular communications and is expected to further evolve towards extra-large-scale MIMO. However, centralized processing for massive MIMO faces practical obstacles, including excessive computational complexity and a substantial volume of baseband data to be exchanged. To address these challenges, decentralized baseband processing has emerged as a promising solution. This approach involves partitioning the antenna array into clusters with dedicated computing hardware for parallel processing. In this paper, we investigate the gradient-based Markov chain Monte Carlo (MCMC) method -- an advanced MIMO detection technique known for its near-optimal performance in centralized implementation -- within the context of a decentralized baseband processing architecture. This decentralized design mitigates the computation burden at a single processing unit by utilizing computational resources in a distributed and parallel manner. Additionally, we integrate the mini-batch stochastic gradient descent method into the proposed decentralized detector, achieving remarkable performance with high efficiency. Simulation results demonstrate substantial performance gains of the proposed method over existing decentralized detectors across various scenarios. Moreover, complexity analysis reveals the advantages of the proposed decentralized strategy in terms of computation delay and interconnection bandwidth when compared to conventional centralized detectors.

翻译：大规模多输入多输出（MIMO）技术显著提升了蜂窝通信的频谱效率和功率效率，并有望进一步向超大规模MIMO演进。然而，大规模MIMO的集中式处理面临实际障碍，包括过高的计算复杂度以及需要交换的大量基带数据。为解决这些挑战，去中心化基带处理已成为一种有前景的解决方案。该方法将天线阵列划分为多个簇，每个簇配备专用计算硬件进行并行处理。本文研究了基于梯度的马尔可夫链蒙特卡洛（MCMC）方法——一种在集中式实现中以其接近最优性能而闻名的先进MIMO检测技术——在去中心化基带处理架构中的应用。这种去中心化设计通过以分布式和并行方式利用计算资源，减轻了单个处理单元的计算负担。此外，我们将小批量随机梯度下降方法集成到所提出的去中心化检测器中，实现了高效且卓越的性能。仿真结果表明，在各种场景下，所提方法相比现有的去中心化检测器均取得了显著的性能提升。此外，复杂度分析揭示了所提出的去中心化策略在计算延迟和互连带宽方面相较于传统集中式检测器的优势。