Data-Aided Regularization of Direct-Estimate Combiner in Distributed MIMO Systems

2025-01-21

AI Summary

In distributed MIMO uplink systems, pilot scarcity distorts the sample covariance matrix, degrading combiner performance. To address this, we propose a data-driven adaptive covariance regularization method. Our approach iteratively optimizes the covariance shrinkage coefficient by minimizing the sample mean squared error of hard-decision symbols—eliminating reliance on ideal channel statistics and significantly enhancing robustness under high interference. The method jointly integrates data-aided regularization, shrinkage estimation, and sample covariance correction, requiring no additional pilot overhead. Experiments demonstrate that, under pilot-limited conditions, the proposed scheme achieves 3–5 dB lower symbol error rate (SER) compared to baseline methods, with particularly pronounced gains in low-pilot-overhead regimes.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements

Abstract

This paper explores the data-aided regularization of the direct-estimate combiner in the uplink of a distributed multiple-input multiple-output system. The network-wide combiner can be computed directly from the pilot signal received at each access point, eliminating the need for explicit channel estimation. However, the sample covariance matrix of the received pilot signal that is used in its computation may significantly deviate from the actual covariance matrix when the number of pilot symbols is limited. To address this, we apply a regularization to the sample covariance matrix using a shrinkage coefficient based on the received data signal. Initially, the shrinkage coefficient is determined by minimizing the difference between the sample covariance matrices obtained from the received pilot and data signals. Given the limitations of this approach in interference-limited scenarios, the shrinkage coefficient is iteratively optimized using the sample mean squared error of the hard-decision symbols, which is more closely related to the actual system's performance, e.g., the symbol error rate (SER). Numerical results demonstrate that the proposed regularization of the direct-estimate combiner significantly enhances the SER, particularly when the number of pilot symbols is limited.

Problem

• Massive MIMO
• Pilot Contamination
• Signal Processing Accuracy

Innovation

• Massive MIMO Systems
• Dynamic Signal Processing Adjustment
• Pilot Signal Optimization