KRAYANI A.

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...

Unmanned Aerial Vehicles (UAVs) attracted both industry and research community owing to their fascinating features like mobility, deployment flexibility and strong Line of Sight (LoS) links. The integration of Cognitive Radio (CR) can greatly help UAVs to overcome several issues especially spectrum scarcity. However, the dynamic radio...

Given the surge in wireless data traffic driven by the emerging Internet of Things (IoT), unmanned aerial vehicles (UAVs), cognitive radio (CR), and non-orthogonal multiple access (NOMA) have been recognized as promising techniques to overcome massive connectivity issues. As a result, there is an increasing need to intelligently improve the...

Future wireless networks will need to improve adaptive resource allocation and decision-making to handle the increasing number of intelligent devices. Unmanned aerial vehicles (UAVs) are being explored for their potential in real-time decision-making. Moreover, cognitive non-orthogonal multiple access (Cognitive-NOMA) is envisioned as a remedy...

Vehicle-to-Everything (V2X) is an emergent technology for enhancing traffic efficiency, road safety and autonomous driving. Vehicles interconnected with their prevalent wireless environment are prone to various security threats that might affect traffic and life safety mmensely. Jamming attacks, a legacy and dated problem, still persists much...

Communication and information field has witnessed recent developments in wireless technologies. Among such emerging technologies, the Internet of Things (IoT) is gaining a lot of popularity and attention in almost every field. IoT devices have to be equipped with cognitive capabilities to enhance spectrum utilization by sensing and learning the...

Classical imitation learning methods suffer substantially from the learning hierarchical policies when the imitative agent faces an unobserved state by the expert agent. To address these drawbacks, we propose an online active learning through active inference approach that encodes the expert's demonstrations based on observation-action to...