Low-Complexity Hybrid Beamforming for Multi-Cell mmWave Massive MIMO: A Primitive Kronecker Decomposition Approach

2025-05-15

AI Summary

To address the joint challenges of high path loss, hardware cost, and inter-cell interference in mmWave multi-cell uplink massive MIMO systems, this paper proposes a low-complexity full-dimensional hybrid beamforming scheme. Methodologically, it introduces a primitive Kronecker decomposition to model uniform planar array geometry, establishes a slow-tuning mechanism under finite-resolution phase shifter constraints, and derives a sufficient condition for optimal antenna configuration from a subspace perspective. A dynamic factor allocation strategy is further designed to jointly optimize analog-domain null-steering beams—suppressing inter-cell interference while enhancing desired signals—and digital-domain MMSE processing—mitigating intra-cell interference—augmented by a low-overhead phase calibration algorithm. Simulation results demonstrate that the proposed scheme achieves sum-rates approaching the fully digital MMSE upper bound, while drastically reducing RF chain count and computational complexity, thereby offering both high spectral efficiency and practical implementation feasibility.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

To circumvent the high path loss of mmWave propagation and reduce the hardware cost of massive multiple-input multiple-output antenna systems, full-dimensional hybrid beamforming is critical in 5G and beyond wireless communications. Concerning an uplink multi-cell system with a large-scale uniform planar antenna array, this paper designs an efficient hybrid beamformer using primitive Kronecker decomposition and dynamic factor allocation, where the analog beamformer applies to null the inter-cell interference and simultaneously enhances the desired signals. In contrast, the digital beamformer mitigates the intra-cell interference using the minimum mean square error (MMSE) criterion. Then, due to the low accuracy of phase shifters inherent in the analog beamformer, a low-complexity hybrid beamformer is developed to slow its adjustment speed. Next, an optimality analysis from a subspace perspective is performed, and a sufficient condition for optimal antenna configuration is established. Finally, simulation results demonstrate that the achievable sum rate of the proposed beamformer approaches that of the optimal pure digital MMSE scheme, yet with much lower computational complexity and hardware cost.

Problem

• Design low-complexity hybrid beamforming for mmWave massive MIMO
• Mitigate inter-cell and intra-cell interference in multi-cell systems
• Reduce hardware cost while maintaining high achievable sum rate

Innovation

• Primitive Kronecker decomposition for hybrid beamforming
• Dynamic factor allocation to reduce interference
• Low-complexity design for analog beamformer adjustment