Splitting Precoding with Subspace Selection and Quantized Refinement for Massive MIMO

TL;DR

This paper introduces a splitting precoding architecture for massive MIMO systems that reduces fronthaul load by dividing the precoding process between the antenna system and baseband unit, improving spectral efficiency.

Contribution

It proposes a novel splitting precoding method with subspace selection and quantized refinement, addressing fronthaul capacity limitations in 5G massive MIMO architectures.

Findings

Achieves higher sum spectral efficiency than conventional precoding.

Reduces quantization losses and signaling overhead.

Effective channel dimension reduction improves performance.

Abstract

Limited fronthaul capacity is a practical bottleneck in massive multiple-input multiple-output (MIMO) 5G architectures, where a base station (BS) consists of an advanced antenna system (AAS) connected to a baseband unit (BBU). Conventional downlink designs place the entire precoding computation at the BBU and transmit a high-dimensional precoding matrix over the fronthaul, resulting in substantial quantization losses and signaling overhead. This letter proposes a splitting precoding architecture that separates the design between the AAS and BBU. The AAS performs a local subspace selection to reduce the channel dimensionality, while the BBU computes an optimized quantized refinement precoding based on the resulting effective channel. The numerical results show that the proposed splitting precoding strategy achieves higher sum spectral efficiency than conventional one-stage precoding.