UAV position uncertainty causes exponential coherence loss in RIS-assisted channels, and a derived threshold predicts when coherent gain collapses and where to deploy the UAV.
When Every Symbol Counts: Resilient Wireless Systems Under Finite Blocklength Constraints
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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.
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2026 1verdicts
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Design and Deployment Guidelines for UAV-Mounted RIS Under Position Uncertainty
UAV position uncertainty causes exponential coherence loss in RIS-assisted channels, and a derived threshold predicts when coherent gain collapses and where to deploy the UAV.