A Novel Resource Allocation for Anti-Jamming in Cognitive-UAVs: An Active Inference Approach
Volume
26
Pagination
2272 - 2276
Publisher
DOI
10.1109/LCOMM.2022.3190971
Journal
IEEE COMMUNICATIONS LETTERS
Issue
ISSN
1089-7798
Metadata
Show full item recordAbstract
This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference (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 AIn approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learning.