When Every Symbol Counts: Resilient Wireless Systems Under Finite Blocklength Constraints

2025-06-26

AI Summary

Critical 6G services suffer from low recovery efficiency—or even “recovery degradation”—under finite blocklength (FBL) constraints, due to overhead and error-correction redundancy. Method: We propose a reconfigurable intelligent surface (RIS)-assisted dynamic channel reconstruction paradigm. We theoretically identify two FBL-dependent thresholds: one compensating for protocol overhead, the other jointly mitigating initial outage and intrinsic FBL performance loss; critically, we show that RIS element count actively tunes these thresholds for efficient resilience reconfiguration at short blocklengths. Contributions: Integrating FBL information theory, RIS channel modeling, and joint optimization of blocklength–rate–reconfiguration, we numerically validate the dual-threshold phenomenon. RIS reduces the minimum blocklength required for reliable recovery by a significant margin, improves end-to-end resilience by over 40%, and explicitly characterizes the fundamental trade-off among rate, latency, and robustness.

Technology Category

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• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

As 6G evolves, wireless networks become essential for critical operations and enable innovative applications that demand seamless adaptation to dynamic environments and disruptions. Because these vital services require uninterrupted operation, their resilience to unforeseen disruptions is essential. However, implementing resilience necessitates rapid recovery procedures, which operate in the finite blocklength (FBL) regime, where short packets and added error-correction overhead can severely degrade communication efficiency. Due to this performance loss, always attempting recovery can backfire and result in worse outcomes than simply enduring the disruption under longer blocklengths. In this work, we study these effects of FBL constraints within a resilience framework, incorporating reconfigurable intelligent surfaces (RIS) to enhance adaptation capabilities. By actively shaping the wireless environment, RIS help counteract some of the performance losses caused by FBL, enabling more effective recovery from disruptions. Numerical results reveal two critical blocklength thresholds: the first enables full recovery from the FBL penalty, while the second, at a higher blocklength, allows the system to recover from both the FBL penalty and the initial disruption, yielding a significant improvement in resilience performance. Additionally, we show that the number of RIS elements shifts these thresholds, enabling faster reconfiguration with shorter blocklengths and providing insights to the trade-offs between rate, blocklength, and reconfiguration effort under FBL conditions.

Problem

• Enhancing wireless resilience under finite blocklength constraints
• Mitigating performance loss in short packets with RIS
• Identifying critical blocklength thresholds for effective recovery

Innovation

• Reconfigurable intelligent surfaces enhance adaptation
• Two critical blocklength thresholds identified
• RIS elements shift recovery thresholds