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

TL;DR

This paper investigates 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, achieving remarkable performance with high efficiency.

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.

Figures (10)

• Figure 1: Block diagram of the uplink massive MIMO system with the DBP architecture. The solid lines depict the connections between CU and DUs in the star DBP topology, while the dashed lines depict the counterparts in the daisy-chain DBP topology.
• Figure 2: Mini-batch gradient-based MCMC detector implemented using the star DBP topology. The implementaion using the daisy-chain topology is similar and thus omitted.
• Figure 3: Convergence performance for a MIMO system configuration of $B=128$ and $U=8$ and $32$ with 16-QAM modulation and ${\text{SNR} = \text{5}\;\text{dB}}$ under IID Rayleigh fading channels.
• Figure 4: BER performance and computational complexity for a MIMO system configuration of $B = 128$, $U = 32$, and $C=32$ with 16-QAM modulation under Rayleigh fading channels. The bar chart counts the number of real-valued multiplications at the CU and a single DU for decentralized schemes.
• Figure 5: BER performance and computational complexity for a MIMO system configuration of $B = 128$, $U = 48$, and $C=32$ with 16-QAM modulation under Rayleigh fading channels.

Theorems & Definitions (6)

• Remark 1
• Remark 2
• Remark 3
• Remark 4
• Remark 5
• Proposition 1