Integrated sensing and communication (ISAC) is a key enabler of sixth-generation (6G) wireless systems, allowing communication and sensing to share hardware and spectrum resources. Unmanned aerial vehicle (UAV) swarms provide a flexible ISAC platform through the use of adaptive threedimensional mobility and distributed coordination. However, jointly optimizing communication throughput, sensing quality, and energy efficiency under realistic hardware impairments will lead to a non-convex and highly-coupled problem. This paper proposes an AI-enhanced, fairness-aware joint sensingcommunication (JSC) framework for UAV swarms operating under phase noise, timing jitter, and power amplifier nonlinearity via an impairment-aware SINR model. A hybrid multi-agent reinforcement learning (MARL)-ADMM architecture enables scalable, distributed optimization under dynamic channel conditions.
Jack Belawske, S. Chakravarty, Imtiaz Ahmed· 2026 International Conferenc...· 0 citations
Module Learning with Errors (MLWE) cryptography normally treats decryption noise as a disturbance: it must be large enough to hide algebraic structure but small enough for reliable decoding. This paper studies a conditional design in which selected residual degrees of freedom also carry coarse physical sensing information. The construction is not presented as a dropin replacement for standardised ML-KEM. Instead, we specify the assumptions under which residual-layer sensing can be analysed, identify what must remain external to FIPS-approved ML-KEM, and give a Fujisaki-Okamoto (FO)/CCA compatibility roadmap. The paper contributes an explicit measurement-to-label pipeline $\boldsymbol{z}=Q(F(y))$, concrete ML-KEM parameter instantiations, residual-budget calculations including compression noise, formal bounds for security-decomposition terms, higher-order leakage bounds beyond balanced mean suppression, an adaptive tuning algorithm, and residual-level Monte Carlo validation. The central message is that structured residual information can be useful for cyber-physical trust only when distributionshape closeness, decoding reliability, sensing privacy, and contextspoofing resistance are all quantified.
Aniket Chakraborty, S. Chakravarty· 2026 International Conferenc...· 0 citations
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