Introducing a data-driven Self-Awareness (SA) module in Cognitive Radio (CR) can support the system to establish secure networks against various attacks from malicious users. Such users manipulate radio spectrum order make CR learn wrong behaviours and take mistaken actions. A basic SA includes ability generative models detect abnormalities inside spectrum. In this work, we propose implement Artificial Intelligence (AI)-based Abnormality Detection techniques at physical (PHY)-layer enabled...

The integration of Cognitive Radio (CR) with Unmanned Aerial Vehicles (UAVs) is an effective step towards relieving the spectrum scarcity and empowering UAV a high degree intelligence. dynamic nature CR dominant line-of-sight links UAVs poses serious security challenges make CR-UAV prone to variety attacks as malicious jamming. Joint jammer detection automatic classification powerful approach against physical layer threats by identifying multiple jammers attacking network that realize...

Unmanned Aerial Vehicles (UAVs) attracted both industry and research community owing to their fascinating features like mobility, deployment flexibility strong Line of Sight (LoS) links. The integration Cognitive Radio (CR) can greatly help UAVs overcome several issues especially spectrum scarcity. However, the dynamic radio environment in CR dependence safe communications from LoS channels integrity UAV make Cognitive- UAV-Radio vulnerable jamming attacks. This work aims study introducing a...

In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios improve PHY security, especially against jamming attacks. SA is based on learning a hierarchical representation of radio environment by means proposed Hierarchical Dynamic Bayesian Network (HDBN). It shown how acquired knowledge from previous experiences facilitate spectrum perception and allow detect abnormal behaviours caused Detecting...

This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and cognitive-UAV is employed as case study. An Generalized Dynamic Bayesian Network (Active-GDBN) proposed to represent the external environment that jointly encodes physical signal dynamics dynamic interaction between UAV jammer spectrum. We cast action planning inference problem can be solved by avoiding surprising states (minimizing abnormality) during...

Vehicle-to-everything (V2X) communication is expected to be a prominent component of the sixth generation (6G) accomplish intelligent transportation systems (ITS). Autonomous vehicles relying only on onboard sensors cannot bypass limitations safety and reliability. Thus, integrated sensing proposed as an effective way achieve high situational- self-awareness levels, enabling V2X perceive physical world adjust its behaviour emergencies. Secure navigation through Global Positioning System...

Classical imitation learning methods suffer substantially from the hierarchical policies when imitative agent faces an unobserved state by expert agent. To address these drawbacks, we propose online active through inference approach that encodes expert's demonstrations based on observation-action to improve learner's future motion prediction. For this purpose, provide a switching Dynamic Bayesian Network dynamic interaction between and another object in its surrounding as reference model,...

Deploying unmanned aerial vehicles (UAVs) as base stations is an exceptional approach to reinforce terrestrial infrastructure owing their remarkable flexibility and superior agility. However, it essential design flight trajectory effectively make the most of UAV-assisted wireless communications. This paper presents a novel method for improving connectivity between UAVs users through effective path planning. achieved by developing goal-directed planning using active inference. First, we...

Cellular connectivity for a massive number of Unmanned Aerial Vehicles (UAVs) will overcrowd the radio spectrum and cause scarcity. Incorporating Cognitive Radio (CR) with UAVs (Cognitive-UAV-Radios) has been proposed to overcome such an issue. However, broadcasting nature CR dominant line-of-sight links UAV makes Cognitive-UAV-Radios susceptible jamming attacks. In this paper, we propose framework detect smart jammer, which locates attacks commands low Jamming-to-Signal-Power-Ratio (JSR)....

Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet Things (IoT) and Vehicle Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable managing mmWave spectrum sharing efficiently. However, Radios are vulnerable malicious users due complex dynamic radio environment shared access medium. This indicates necessity implement techniques able detect precisely any anomalous behaviour in build secure efficient radios. work, we propose comparison...

Equipping autonomous agents for dynamic interaction and navigation is a significant challenge in intelligent transportation systems. This study aims to address this by implementing brain-inspired model decision making vehicles. We employ active inference, Bayesian approach that models decision-making processes similar the human brain, focusing on agent’s preferences principle of free energy. combined with imitation learning enhance vehicle’s ability adapt new observations make human-like...

Active inference is a probabilistic framework for modeling intelligent agent behaviours, which drives by the principle of minimizing free energy. In this paper, we integrate imitation learning method with active to minimize expected energy under supervision an expert model. The proposed approach affords explainable decision-making as combination self-information and novelty-seeking or exploratory behavior in hierarchical generative A lane-changing driving scenario demonstrated verify...

This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which UAV can learn world model understand environment implicitly discover optimizer's policy. equipped with make real-time autonomous decisions engage in online planning using active inference. During planning, score different policies based on expected surprise, allowing it...

The proliferation of interconnected objects in the Internet Things (IoT) can be benefit from integration cognitive radio (CR) technologies at network level. IoT networks equipped with capabilities help to effectively alleviate problem spectrum scarcity. However, suffer jammer attacks that interfere user transmissions and disrupt communications. In this paper, we consider a CR-IoT based on Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme reactive is hypothesized present...

This paper proposes a novel Automatic Modulation Classification (AMC) method for CR-IoT based on learning multiple Generalized Dynamic Bayesian Networks (GDBN) as representations of various signals under different modulation schemes. The performs predictions online in parallel and evaluates abnormality measurements Modified Markov Jump Particle Filter (M-MJPF) to select the best model that explains received signal recognize scheme accordingly. simulated results real dataset demonstrate...

Vehicle-to-Everything (V2X) is an emergent technol-ogy 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 life immensely. Jamming attacks, a legacy dated problem, still persists much the havoc of V2X communications. The following paper proposes framework jammer detection adapted communications scenario. A Generalized Dynamic Bayesian network used learn...

Communication and information field has witnessed recent developments in wireless technologies. Among such emerging technologies, the Internet of Things (IoT) is gaining a lot popularity attention almost every field. IoT devices have to be equipped with cognitive capabilities enhance spectrum utilization by sensing learning surrounding environment. network susceptible various jamming attacks which interrupt users communication. In this paper, two systems (Single Bank-Parallel) been proposed...

The following paper proposes a novel Vehicle-to-Everything (V2X) network abnormality detection scheme based on Bayesian generative models for enhanced self-awareness functionality at the Base station (BS). In learning phase, multi-modal data signals contrived by vehicles' integrated and sensing module are imbued into data-driven Generalized Dynamic (GDBN) models. Following that, during testing an Interactive Modified Markov Jump Particle filter (IM-MJPF) is utilized to forecast forthcoming...

This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which UAV can learn world model understand environment implicitly discover optimizer's policy. equipped with make real-time autonomous decisions engage in online planning using active inference. During planning, score different policies based on expected surprise, allowing it...