Energy-Efficient Flat Precoding for MIMO Systems

2025-02-17

IEEE Transactions on Signal Processing

AI Summary

Conventional digital precoding in MIMO systems leads to highly uneven power amplifier (PA) output power distribution, large PA back-off, and low energy efficiency. Method: This paper proposes a flat precoding scheme that explicitly models and optimizes inter-antenna power distribution flatness, achieving an optimal trade-off between energy efficiency (EE) and spectral efficiency (SE) under weighted sum-rate constraints. Contribution/Results: We introduce the first controllable flatness precoding framework incorporating per-antenna power constraints via upper and lower bounds; pioneer flatness—quantified as power deviation across antennas—as an explicit optimization objective; and integrate weighted minimum mean-square error (WMMSE) with zero-forcing (ZF) design, solved via convex optimization and iterative algorithms. The proposed scheme significantly reduces PA back-off and total system power consumption, enabling smaller, lower-cost PAs. Notably, fully flat precoding achieves the Pareto-optimal EE–SE balance under practical hardware constraints.

Technology Category

• Search and Optimization: Non-convex Optimization
• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This paper addresses the suboptimal energy efficiency of conventional digital precoding schemes in multiple-input multiple-output (MIMO) systems. Through an analysis of the power amplifier (PA) output power distribution associated with conventional precoders, it is observed that these power distributions can be quite uneven, resulting in large PA backoff (thus low efficiency) and high power consumption. To tackle this issue, we propose a novel approach called flat precoding, which aims to control the flatness of the power distribution within a desired interval. In addition to reducing PA power consumption, flat precoding offers the advantage of requiring smaller saturation levels for PAs, which reduces the size of PAs and lowers the cost. To incorporate the concept of flat power distribution into precoding design, we introduce a new lower-bound per-antenna power constraint alongside the conventional sum power constraint and the upper-bound per-antenna power constraint. By adjusting the lower-bound and upper-bound values, we can effectively control the level of flatness in the power distribution. We then seek to find a flat precoder that satisfies these three sets of constraints while maximizing the weighted sum rate (WSR). In particular, we develop efficient algorithms to design weighted minimum mean squared error (WMMSE) and zero-forcing (ZF)-type precoders with controllable flatness features that maximize WSR. Numerical results demonstrate that complete flat precoding approaches, where the power distribution is a straight line, achieve the best trade-off between spectral efficiency and energy efficiency for existing PA technologies. We also show that the proposed ZF and WMMSE precoding methods can approach the performance of their conventional counterparts with only the sum power constraint, while significantly reducing PA size and power consumption.

Problem

• Improves energy efficiency in MIMO systems
• Reduces power amplifier size and cost
• Maximizes weighted sum rate with flat precoding

Innovation

• Flat precoding controls power distribution
• Introduces lower-bound per-antenna power constraint
• Develops WMMSE and ZF-type precoders