论文标题
一种新颖的资源分配，用于抗束缚中的认知 - UAV：一种主动推断方法
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach
论文作者
论文摘要
这项工作提出了一种新型的资源分配策略，用于使用主动推断（$ \ textit {ain} $）在认知无线电中进行抗束缚，并采用了认知-UAV作为案例研究。提出了一个主动的广义动态贝叶斯网络（Active-GDBN），以代表共同编码物理信号动力学的外部环境以及频谱中无人机和干扰器之间的动态相互作用。我们将行动和计划作为贝叶斯推论问题进行了策划，可以通过避免在线学习期间（最小化异常）来解决。仿真结果验证了提出的$ \ textit {ain} $方法在最小化异常（最大化奖励）方面的有效性，并通过将其与常规的频率跳跃和Q学习进行比较，具有高收敛速度。
This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\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.
