Quantized Precoding for Maximizing Sum Rate in MU-MIMO Systems with Constrained Fronthaul

2026-02-27

AI Summary

This work addresses the performance degradation caused by quantized precoding in MU-MIMO downlink systems under limited fronthaul capacity. To tackle this challenge, the authors propose a quantization-aware sum-rate maximization framework that explicitly incorporates quantization constraints into the precoder design. The approach integrates a WMMSE-inspired iterative optimization procedure with integer least-squares refinement, sphere decoding, and a low-complexity expectation propagation (EP) algorithm, achieving an effective trade-off between performance and computational complexity. Experimental results demonstrate that the proposed scheme significantly outperforms conventional “design-then-quantize” baselines. Notably, the EP-based implementation closely approaches the optimal performance while substantially reducing computational overhead.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design
• Computer Vision: Learning & Optimization for CV

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This paper studies a downlink multi-user multiple-input multiple-output (MU-MIMO) system, where the precoding matrix is computed at a baseband unit (BBU) and then transmitted to the remote antenna array over a limited-capacity digital fronthaul. The limited bit resolution of the fronthaul introduces quantization effects that are explicitly modeled. We propose a novel sum rate maximization framework that directly incorporates the quantizer's constraints into the precoding design. The resulting maximization problem, a non-convex mixed-integer program, is addressed using a new iterative algorithm inspired by the weighted minimum mean square error (WMMSE) methodology. The precoding optimization subproblem is reformulated as an integer least-squares problem and solved using a novel sphere decoding (SD) algorithm. Additionally, a low-complexity expectation propagation (EP)-based method is introduced to enable the practical implementation of quantized precoding in MU-massive MIMO (MU-mMIMO) systems. Furthermore, numerical evaluations demonstrate that the proposed precoding schemes outperform conventional approaches that optimize infinite-resolution precoding followed by element-wise quantization. We also propose a heuristic quantization-aware precoding method with comparable complexity to the baseline but superior performance. In particular, the EP-based approach offers near-optimal performance with substantial complexity reduction, making it well-suited for real-time MU-mMIMO applications.

Problem

• MU-MIMO
• quantized precoding
• fronthaul constraint
• sum rate maximization
• limited-capacity fronthaul

Innovation

• Quantized Precoding
• Fronthaul Constraint
• WMMSE
• Sphere Decoding
• Expectation Propagation