Distributed Signal Processing for Extremely Large-Scale Antenna Array Systems: State-of-the-Art and Future Directions

TL;DR

The paper surveys distributed signal processing algorithms tailored for extremely large-scale antenna array (ELAA) systems, addressing interconnection costs, computational complexity, and synchronization/calibration bottlenecks. It articulates three representative ELAA forms—single-BS ELAA, coordinated distributed antennas, and ELAA with emerging technologies—and details distributed SP methods for each form, including DCE, MUE/LCP, and VPC-based precoding, with attention to fronthaul constraints and near-field effects. It highlights challenges and future directions such as near-field distributed SP, RIS/ISAC integration, low-resolution hardware, and CF-mMIMO splits, stressing the need for scalable, robust algorithms. The practical impact lies in enabling scalable, high-capacity ELAA deployments for 6G and beyond through DBP architectures and topology-aware distributed processing, while mitigating fronthaul and computation burdens.

Abstract

Extremely large-scale antenna arrays (ELAA) play a critical role in enabling the functionalities of next generation wireless communication systems. However, as the number of antennas increases, ELAA systems face significant bottlenecks, such as excessive interconnection costs and high computational complexity. Efficient distributed signal processing (SP) algorithms show great promise in overcoming these challenges. In this paper, we provide a comprehensive overview of distributed SP algorithms for ELAA systems, tailored to address these bottlenecks. We start by presenting three representative forms of ELAA systems: single-base station ELAA systems, coordinated distributed antenna systems, and ELAA systems integrated with emerging technologies. For each form, we review the associated distributed SP algorithms in the literature. Additionally, we outline several important future research directions that are essential for improving the performance and practicality of ELAA systems.

Figures (8)

• Figure 1: Illustrations of representative forms of ELAA systems (top row) and four strategies with relevant technologies (bottom row).
• Figure 2: The fronthaul costs (Fig. \ref{['fig: fronthaul cost']}) and computational complexities (Fig. \ref{['fig: complexity']}) of the centralized channel estimation algorithm and equalization algorithm based on the LMMSE criteria, where we use a setting of $192$ subcarriers (i.e., $16$ resource blocks), $32$ users, and $140$ symbols.
• Figure 3: Single-BS ELAA system with antenna clustering-based DBP architecture.
• Figure 4: Illustration of the signal exchange processes of the AGE-based algorithm in (a) the star network and (b) the daisy-chain network.
• Figure 5: Illustrations of the DBP architecture.