Design Trade-offs for Decentralized Baseband Processing in Massive MU-MIMO Systems

TL;DR

This paper systematically analyzes the trade-offs in decentralized baseband processing architectures for massive MU-MIMO systems, focusing on error performance, complexity, and latency to guide optimal design choices.

Contribution

It provides practical guidelines for selecting DBP architectures and algorithms considering system configurations, channel conditions, and implementation aspects.

Findings

Decentralized processing reduces interconnect bandwidth requirements.

Trade-offs between error rate, complexity, and latency are characterized.

Guidelines enable optimized DBP design for massive MU-MIMO systems.

Abstract

Massive multi-user (MU) multiple-input multiple-output (MIMO) provides high spectral efficiency by means of spatial multiplexing and fine-grained beamforming. However, conventional base-station (BS) architectures for systems with hundreds of antennas that rely on centralized baseband processing inevitably suffer from (i) excessive interconnect data rates between radio-frequency circuitry and processing fabrics, and (ii) prohibitive complexity at the centralized baseband processor. Recently, decentralized baseband processing (DBP) architectures and algorithms have been proposed, which mitigate the interconnect bandwidth and complexity bottlenecks. This paper systematically explores the design trade-offs between error-rate performance, computational complexity, and data transfer latency of DBP architectures under different system configurations and channel conditions. Considering architecture, algorithm, and numerical precision aspects, we provide practical guidelines to select the DBP architecture and algorithm that are able to realize the full benefits of massive MU-MIMO in the uplink and downlink.