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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. [ABSTRACT FROM AUTHOR]

Technologies such as cognitive radio and dynamic spectrum access rely on spectrum sensing which provides wireless devices with information about the radio spectrum in the surrounding environment. One of the main challenges in wireless communications is the interference caused by malicious users on the shared spectrum. In this manuscript, an artificial intelligence enabled cognitive radio framework is proposed at system-level as part of a cyclic spectrum intelligence algorithm for interference mitigation in wideband radios. It exploits the cyclostationary feature of signals to differentiate users with different modulation schemes and an artificial neural network as classifier to detect potential malicious users. A dataset consisting of experimental modulated and dynamic signals is recorded by spectrum measurements with an in-house software defined radio testbed and then processed. Cyclostationary features are extracted for each detected signal and fed to a neural network classifier as training and testing data in a complex and dynamic scenario. Results highlight a classification rate of ∼ 1 in most of cases, even at low transmission power. A comparison with two previous works with hand-crafted features, which employ an energy detector-based classifier and a naive Bayes-based classifier, respectively, is discussed. [ABSTRACT FROM AUTHOR]

Cognitive radio is a promising technology for frequency allocation to improve the spectrum utilization efficiency of licensed bands. However, in recent years, the attention of the researchers is focused on security issues that have to be faced by cognitive radio technology. One of the most important issues consists of radio frequency jamming attacks, where adversaries can use on-the-fly reconfigurability and learning capabilities of cognitive radios in order to devise and deploy advanced jamming tactics. Jamming attacks can noticeably affect the performance of wireless communication systems and can lead to significant overhead in terms of data re-transmission and increased power consumption. In this article, a novel compressed sensing-based jammer detection algorithm is proposed using cyclic spectral analysis and artificial neural networks for wideband cognitive radios. A wideband spectrum is considered that is composed of multiple narrowband signals. Narrowband signals can be legitimate or jamming signals. Compressed sensing is used to reduce the overhead of the analog-to-digital conversion and it allows one to estimate a wideband spectrum with sub-Nyquist rate sampling. After the signal has been estimated, the second-order statistics, namely, spectral correlation function, is computed to extract cyclic features of the wideband signal. Finally, a pre-trained artificial neural network is proposed to classify each narrowband signal as a legitimate or jamming signal. Performances of proposed algorithm are shown with Monte-Carlo simulations under different empirical setups. [ABSTRACT FROM AUTHOR]