A New Hybrid Precoding Approach for Multi-user Massive MIMO over Fading Channels

2025-10-28

AI Summary

In multi-user massive MIMO systems operating over fading channels, strong sidelobe interference and challenges in dynamic channel modeling severely degrade performance. To address these issues, this paper proposes a joint angle–phase-aware hybrid precoding scheme. Departing from conventional approaches, it models the angle of arrival (AoA) and phase as correlated binary Gaussian variables and introduces a joint angle–phase entropy metric to quantify their coupled uncertainty. This entropy-guided framework enables coordinated optimization of digital baseband and analog radio-frequency precoders. The resulting design significantly enhances beam pointing accuracy and channel adaptability. Simulation results demonstrate that the proposed method achieves an 18.31% gain in system sum rate and an 11.47% improvement in robustness compared to state-of-the-art benchmarks.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search

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
• Security and Privacy: Large-scale security measurements

Abstract

Hybrid precoding is an indispensable technique to harness the full potential of a multi-user massive multiple-input, multiple-output (MU-MMIMO) system. In this paper, we propose a new hybrid precoding approach that combines digital and analog precoding to optimize data transmission over multiple antennas. This approach steers signals in specific directions, leading to maximizing sum-rate and suppressing side-lobe interference. When dealing with complex signals, changes in phase are naturally associated with changes in angle, and these variations are inherently correlated. The correlation between the angle and phase is essential for accurately determining the channel characteristics. An important aspect of this approach is that we model the angle and phase as correlated variables following a bivariate Gaussian distribution, and for the first time, we define a joint angle and phase entropy to measure the uncertainty of angle and phase variations in wireless channels. This entropy is crucial to adapt the proposed precoding method with variations. Simulation result validate the accuracy of our analytical findings, demonstrating 18.31% increase in sum-rate and an 11.47% improvement in robustness compared to other state-of-the-art methods.

Problem

• Optimizing hybrid precoding for multi-user massive MIMO systems
• Maximizing sum-rate and suppressing interference in fading channels
• Modeling correlated angle-phase variations to improve transmission robustness

Innovation

• Hybrid precoding combines digital and analog techniques
• Models angle and phase as correlated Gaussian variables
• Defines joint angle-phase entropy to measure uncertainty