Mini-Batch Gradient-Based MCMC for Decentralized Massive MIMO Detection

TL;DR

This paper introduces a decentralized MIMO detection method using mini-batch gradient-based MCMC, significantly reducing computational complexity and data exchange while maintaining near-optimal detection performance.

Contribution

It proposes a novel decentralized detection framework that integrates mini-batch stochastic gradient descent with gradient-based MCMC for massive MIMO systems.

Findings

Substantial performance improvements over existing decentralized detectors.

Reduced computation delay and interconnection bandwidth.

Effective scalability for extra-large-scale MIMO systems.

Abstract

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.