A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach

TL;DR

This paper introduces a novel resource allocation strategy using Active Inference for anti-jamming in Cognitive Radio, employing a cognitive-UAV as a case study, and demonstrates its effectiveness through simulations.

Contribution

It proposes an Active Generalized Dynamic Bayesian Network for environment modeling and casts resource allocation as a Bayesian inference problem, which is a new approach in this context.

Findings

Effective in minimizing abnormalities and maximizing rewards

High convergence speed compared to traditional methods

Outperforms Frequency Hopping and Q-learning in simulations

Abstract

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.