Decentralized Equalization with Feedforward Architectures for Massive MU-MIMO

TL;DR

This paper introduces decentralized feedforward architectures for equalization in massive MU-MIMO systems, reducing latency and data transfer bottlenecks while maintaining high throughput and near-centralized performance.

Contribution

It proposes two novel decentralized feedforward equalization architectures that lower interconnect bandwidth and latency in massive MU-MIMO base stations.

Findings

Achieves Gb/s throughput with decentralized architectures

Maintains near-centralized performance levels

Demonstrates effectiveness on GPU systems

Abstract

Linear data-detection algorithms that build on zero forcing (ZF) or linear minimum mean-square error (L-MMSE) equalization achieve near-optimal spectral efficiency in massive multi-user multiple-input multiple-output (MU-MIMO) systems. Such algorithms, however, typically rely on centralized processing at the base-station (BS) which results in (i) excessive interconnect and chip input/output (I/O) data rates and (ii) high computational complexity. Decentralized baseband processing (DBP) partitions the BS antenna array into independent clusters that are associated with separate radio-frequency circuitry and computing fabrics in order to overcome the limitations of centralized processing. In this paper, we investigate decentralized equalization with feedforward architectures that minimize the latency bottlenecks of existing DBP solutions. We propose two distinct architectures with different interconnect and I/O bandwidth requirements that fuse the local equalization results of each cluster in a feedforward network. For both architectures, we consider maximum ratio combining, ZF, L-MMSE, and a nonlinear equalization algorithm that relies on approximate message passing, and we analyze the associated post-equalization signal-to-noise-and-interference-ratio (SINR). We provide reference implementation results on a multi graphics processing unit (GPU) system which demonstrate that decentralized equalization with feedforward architectures enables throughputs in the Gb/s regime and incurs no or only a small performance loss compared to centralized solutions.