On the Massive MIMO Channel Polarization

2025-01-23

AI Summary

Achieving near-Shannon capacity in massive MIMO systems under stringent power constraints and extremely high channel dimensionality remains challenging. Method: This paper establishes, for the first time, an intrinsic channel polarization phenomenon: singular value decomposition (SVD) or QR decomposition of the massive MIMO channel matrix yields highly polarized subchannel qualities—i.e., a bimodal distribution of strong and weak subchannels. Leveraging this insight, we propose a low-complexity polarized precoding scheme that freezes weak subchannels and allocates power only to strong ones under total power constraint. Contribution/Results: The proposed scheme achieves significantly higher ergodic capacity and lower bit error rate than conventional channel equalization, while outperforming lattice-reduction (LR)-aided precoding in both performance and efficiency. It reduces computational complexity and CSI feedback overhead without sacrificing capacity逼近. This work provides a novel theoretical perspective and a practical, efficient paradigm for Shannon-capacity approximation in massive MIMO systems.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Search and Optimization: Non-convex Optimization
• Planning, Routing, and Scheduling: Applications

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In this work, we demonstrate that an $n imes n$ massive multiple-input multiple-output (MIMO) channel can be polarized using common matrix decomposition techniques: singular value decomposition (SVD) and QR decomposition. With full channel state information (CSI), we show that channel capacity is always attained by freezing certain number of worst subchannels, provided a total power constraint and sufficiently large $n$. We further prove that the capacity obtained through channel polarization is always greater than that achieved through channel equalization. Finally, we propose a low-complexity precoding scheme based on channel polarization, which outperforms the lattice-reduction-aided precoding scheme, in terms of capacity, decoding error rate, encoding complexity, and CSIT cost.

Problem

• Large-scale MIMO
• Polarization Phenomenon
• Signal Transmission Optimization

Innovation

• Polarization Phenomenon
• Optimized Signal Transmission
• Novel Precoding Scheme