Bounded-Latency Change Detection of Channel Covariance Matrix in Real-Time MIMO Communications

The accuracy of the channel covariance matrix (CCM) critically determines MIMO system performance. While CCM typically evolves gradually, abrupt changes may occur in practice, necessitating immediate detection for timely updates. Conventional detectors risk unbounded delays under minor variations, violating protocol latency constraints. To address this challenge, we propose a transient change detection (TCD) framework that integrates fixed-sample-size (FSS) sampling with optimized window configuration, ensuring strict detection deadlines while minimizing missed detection probabilities. Experimental results show significant reduction in detection delay and improved computational efficiency while maintaining high accuracy compared to benchmarks, demonstrating its efficacy for real-time MIMO applications.

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Publication Details

Published
2025-08-10
DOI
https://doi.org/10.1109/iccc65529.2025.11148606
Citations
1
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.73
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Bounded-Latency Change Detection of Channel Covariance Matrix in Real-Time MIMO Communications

Yin Xu, Chang Wen Chen, Runnan Liu, Dazhi He et al.
1 citations
Advanced MIMO Systems Optimization
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article

Bounded-Latency Change Detection of Channel Covariance Matrix in Real-Time MIMO Communications

Yin Xu, Chang Wen Chen, Runnan Liu, Dazhi He, Zehao Chen, Xiaowu Ou, Wenjia Zhang, Cixiao Zhang
article en
1 citations

Abstract

The accuracy of the channel covariance matrix (CCM) critically determines MIMO system performance. While CCM typically evolves gradually, abrupt changes may occur in practice, necessitating immediate detection for timely updates. Conventional detectors risk unbounded delays under minor variations, violating protocol latency constraints. To address this challenge, we propose a transient change detection (TCD) framework that integrates fixed-sample-size (FSS) sampling with optimized window configuration, ensuring strict detection deadlines while minimizing missed detection probabilities. Experimental results show significant reduction in detection delay and improved computational efficiency while maintaining high accuracy compared to benchmarks, demonstrating its efficacy for real-time MIMO applications.

Hong Kong Polytechnic University (HK), Shanghai Jiao Tong University (CN), Shandong Jiaotong University (CN), China Academy of Information and Communications Technology (CN)
Openalex Percentile: Top 24%
Advanced MIMO Systems Optimization
0.73
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