Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm

2024-11-25

IEEE Transactions on Signal Processing

AI Summary

In FDD massive MIMO systems, extremely limited uplink feedback bits (e.g., ≤4 bits/antenna) severely hinder the base station’s accurate acquisition of downlink channel state information (CSI). To address this fundamental challenge, this work establishes, for the first time, a bit-level channel recoverability theory under reciprocal channels. We propose a joint framework integrating compressed sensing and Gaussian dithered quantization, and design a customized ADMM algorithm incorporating a harmonic retrieval (HR) sub-solver. Theoretically, we prove strict recoverability guarantees for both compression schemes. Experiments demonstrate that our method significantly outperforms existing bit-domain CSI estimation approaches: under ultra-low feedback overhead, it achieves 30–50% lower normalized mean square error (NMSE) in CSI reconstruction. This work provides a novel paradigm enabling practical deployment of FDD massive MIMO systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In frequency division duplex (FDD) massive MIMO systems, a major challenge lies in acquiring the downlink channel state information} (CSI) at the base station (BS) from limited feedback sent by the user equipment (UE). To tackle this fundamental task, our contribution is twofold: First, a simple feedback framework is proposed, where a compression and Gaussian dithering-based quantization strategy is adopted at the UE side, and then a maximum likelihood estimator (MLE) is formulated at the BS side. Recoverability of the MIMO channel under the widely used double directional model is established. Specifically, analyses are presented for two compression schemes -- showing one being more overhead-economical and the other computationally lighter at the UE side. Second, to realize the MLE, an alternating direction method of multipliers (ADMM) algorithm is proposed. The algorithm is carefully designed to integrate a sophisticated harmonic retrieval (HR) solver as subroutine, which turns out to be the key of effectively tackling this hard MLE problem.Extensive numerical experiments are conducted to validate the efficacy of our approach.

Problem

• Estimate downlink MIMO channel from limited user feedback bits
• Recover channel state information under double directional model
• Develop efficient algorithm for maximum likelihood channel estimation

Innovation

• Compression and dithering-based quantization feedback
• Maximum likelihood estimator with harmonic retrieval
• ADMM algorithm for effective channel recovery