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.
翻译:这项工作提出了使用主动推断值($\textit{AIn}$)在认知无线电中进行反干扰的新资源分配战略,并使用认知-无人驾驶航空器作为案例研究。提议建立一个活跃的通用动态巴伊西亚网络(Apact-GDBN)来代表外部环境,将无人驾驶航空器与干扰器在频谱中的物理信号动态和动态互动联合编码起来。我们把行动和规划视为一种巴伊西亚推论问题,通过在在线学习中避免出乎意料的状态(尽量减少异常),可以解决这个问题。模拟结果验证了拟议中的$textit{AIn}方法在尽量减少异常现象(最大回报)方面的有效性,并且通过将它与常规频率震动和Q学习进行比较而具有高度的趋同速度。