A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach
This addresses anti-jamming for cognitive-UAVs, presenting a novel method for a known bottleneck in resource allocation.
The paper tackles the problem of anti-jamming resource allocation in cognitive-UAVs by proposing an Active Inference approach, which minimizes abnormalities and achieves high convergence speed compared to conventional methods like Frequency Hopping and Q-learning.
This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network (Active-GDBN) is proposed to represent the external environment that jointly encodes the physical signal dynamics and the dynamic interaction between UAV and jammer in the spectrum. We cast the action and planning as a Bayesian inference problem that can be solved by avoiding surprising states (minimizing abnormality) during online learning. Simulation results verify the effectiveness of the proposed $\textit{AIn}$ approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learning.