AI & ML
Stealthy Malicious Interference Detection in UAV Formations: A Power-Domain Signaling Game Approach
arXiv math.IT0 views
arXiv:2611.07977v1 Announce Type: cross Abstract: Reliable intra-swarm communication is fundamental to cooperative sensing, control, and mission execution in unmanned aerial vehicle (UAV) swarms. In open low-altitude shared-spectrum environments, coexisting UAVs may cause benign interference through spectrum reuse and signal leakage, while jamming UAVs may deliberately degrade critical intra-swarm links. Since a jamming UAV can adjust its transmit power to disguise signal-to-interference-plus-noise ratio (SINR) degradation as link fluctuation in benign interference, stealthy jammer UAV detection remains a challenge. In this paper, we propose a novel belief-driven stealthy jammer UAV detection scheme from the perspective of Bayesian security game. To capture stealthy interference in shared-spectrum UAV swarms, the interfering UAV is modeled as a hidden benign or jamming type, whose transmit power strategy induces the SINR observed by the defender. The defender updates its posterior belief from the observed SINR and decides whether to maintain normal operation or trigger defensive power boosting. We characterize pure-strategy perfect Bayesian equilibrium (PBE) conditions under separating structures with distinguishable type and pooling structures with indistinguishable observations. Numerical results show that the proposed scheme improves the defender's expected utility compared to the conventional schemes.
Source:https://arxiv.org/abs/2611.07977PreviousJoint Flexible Beamforming, Resource Allocation, and Task Buffering for Movable-Antenna Enabled MEC SystemsNextThe probabilistic bound for perfect hashing is sharp only for binary alphabets
Related posts
Geometric Functional Inequalities for Markov Processes: An Invitation via the Langevin Diffusion
0arXiv stat.CO
A Unified Coordinate Descent Framework for Penalized Longitudinal Log-Contrast Quantile Regression
0arXiv stat.CO
Fast Functional Clustering via Closed-Form Covariance Updates
0arXiv stat.CO
marp: Model-Averaged Inference for Parametric Renewal Processes in R
0arXiv stat.CO
dsp: An R Package for Bayesian Time Series Modeling with the Dynamic Shrinkage Process
0arXiv stat.CO
Machine Learning Fundamentals: Understanding How Machines Learn from Data
0dev.to Discuss