Abstract--This paper proposes a hierarchical trajectory planning framework for UA Vs operating under adversarial jamming conditions. Leveraging Bayesian Active Inference, the approach combines expert-generated demonstrations with probabilistic generative modeling to encode high-level symbolic planning, low-level motion policies, and wireless signal feedback. During deployment, the UA V performs online inference to anticipate interference, localize jammers, and adapt its trajectory accordingly--without prior knowledge of jammer locations. Simulation results demonstrate that the proposed method achieves near-expert performance, significantly reducing communication interference and mission cost compared to model-free reinforcement learning baselines, while maintaining robust generalization in dynamic environments. Unmanned Aerial V ehicles (UA Vs) play a crucial role in military, public, and civilian applications due to their compact size, flexible deployment capabilities, and outstanding performance.

This work presents an e xperimental performance evaluation of a p rivate 5G a irfield n etwork under controlled directional SDR jamming attacks targeting UAV - based UE nodes . Using a QualiPoc Android UE, mounted as a payload on a quad-copter UAV, we conducted a series of experiments to evaluate signal degradation, handover performance, and service stability in the presence of constant directional jamming. The conducted experiments aimed to examin e the effe c t s of varying travel speed s, altitudes, and moving patterns of a UAV - based UE to record and analyze the key physical - layer and network - layer metrics such as CQI, MCS, RSRP, SINR, BLER, Net PDSCH Throughput and RLF. The results of this work describe the link stability and signal degradation dependencies, caused by the level of mobility of the UAV - based UE nodes during autonomous and automatic operation in private 5G Airfield networks.

We propose the use of adversarial wireless jamming to regularise the latent space of an autoencoder to match a diagonal Gaussian distribution. We consider the minimisation of a mean squared error distortion, where a jammer attempts to disrupt the recovery of a Gaussian source encoded and transmitted over the adversarial channel. A straightforward consequence of existing theoretical results is the fact that the saddle point of a minimax game - involving such an encoder, its corresponding decoder, and an adversarial jammer - consists of diagonal Gaussian noise output by the jammer. We use this result as inspiration for a novel approach to distribution matching in the latent space, utilising jamming as an auxiliary objective to encourage the aggregated latent posterior to match a diagonal Gaussian distribution. Using this new technique, we achieve distribution matching comparable to standard variational autoencoders and to Wasserstein autoencoders. This approach can also be generalised to other latent distributions.

--Networks built on the IEEE 802.11 standard have experienced rapid growth in the last decade. Their field of application is vast, including smart home applications, Internet of Things (IoT), and short-range high throughput static and dynamic inter-vehicular communication networks. Within such networks, Channel State Information (CSI) provides a detailed view of the state of the communication channel and represents the combined effects of multipath propagation, scattering, phase shift, fading, and power decay. In this work, we investigate the problem of jamming attack detection in static and dynamic vehicular networks. We utilize ESP32-S3 modules to set up a communication network between an Unmanned Aerial V ehicle (UA V) and a Ground Control Station (GCS), to experimentally test the combined effects of a constant jammer on recorded CSI parameters, and the feasibility of jamming detection through CSI analysis in static and dynamic communication scenarios. The rapid expansion of IEEE 802.11 networks over the past decade has revolutionized wireless communications, particularly in such applications as smart homes [1], Internet of Things (IoT) [2], industrial automation, and short-range high-throughput vehicular networks [3]. This can be contributed to their high throughput capabilities, ease of deployment, and increasingly growing demand for internet connectivity. However, the widespread usage and extensive deployment of these networks make them an attractive target for malicious actors, and thus, more exposed and susceptible to jamming attacks.

Abstract--Mobile jammers pose a critical threat to 5G networks, particularly in military communications. This paper investigates an anti-jamming framework that enhances a strong adaptive beamforming baseline comprising Multiple Signal Classification (MUSIC) for Direction-of-Arrival (DoA) estimation and Minimum V ariance Distortionless Response (MVDR) for interference suppression with a lightweight machine learning (ML) model for predictive error correction. Extensive simulations in a realistic highway scenario demonstrate that the integrated system achieves a high DoA estimation accuracy of up to 99.8% and an average Signal-to-Noise Ratio (SNR) improvement of 9.58 dB. Analysis reveals that the MUSIC-MVDR baseline alone accounts for the vast majority of this performance gain (9.46 dB), indicating that the primary benefit of the simple ML model lies in correcting outlier estimates rather than providing a substantial systemic SNR increase. The framework's computational efficiency validates the effectiveness of the core beamforming approach and highlights the critical trade-off between ML model complexity and practical performance gains for securing 5G communications in contested environments.

ABSTRACT We propose an active jammer localization framework that combines Bayesian optimization with acquisition-aware path planning. Unlike passive crowdsourced methods, our approach adaptively guides a mobile agent to collect high-utility Received Signal Strength measurements while accounting for urban obstacles and mobility constraints. For this, we modified the A* algorithm, A-UCB*, by incorporating acquisition values into trajectory costs, leading to high-acquisition planned paths. Simulations on realistic urban scenarios show that the proposed method achieves accurate localization with fewer measurements compared to uninformed baselines, demonstrating consistent performance under different environments. Index T erms-- Jammer localization, GNSS interference, Bayesian optimization, Gaussian processes, Path planning 1. INTRODUCTION Global Navigation Satellite Systems (GNSS) such as GPS, Galileo, GLONASS and BeiDou provide critical position, navigation, and timing (PNT) services for a wide array of applications, from intelligent transportation and precision agriculture to timing-dependent infrastructures like banking systems and cellular networks [1].

Abstract--This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmissions. The transmitter-receiver pair learns to avoid jamming and optimize throughput over time (without prior knowledge of channel conditions or jamming strategies) by using reinforcement learning (RL) to adapt transmit power, modulation, and channel selection. Q-learning is employed for discrete jamming-event states, while Deep Q-Networks (DQN) are employed for continuous states based on received power . Through different reward functions and action sets, the results show that RL can adapt rapidly to spectrum dynamics and sustain high rates as channels and jamming policies change over time. The open wireless medium is inherently vulnerable to intentional interference, allowing malicious actors to degrade or even deny service across commercial and tactical networks.

As drones have risen to prominence on the battlefield, so too has electronic warfare, in which adversaries attempt to mask, jam or trace radio signals.

--In this paper, a novel covert semantic communication framework is investigated. An attacker seeks to detect and eavesdrop the semantic transmission to acquire details of the original image. T o avoid data meaning being eavesdropped by an attacker, a friendly jammer is deployed to transmit jamming signals to interfere the attacker so as to hide the transmitted semantic information. Meanwhile, the server will strategically select time slots for semantic information transmission. Due to limited energy, the jammer will not communicate with the server and hence the server does not know the transmit power of the jammer . Therefore, the server must jointly optimize the semantic information transmitted at each time slot and the corresponding transmit power to maximize the privacy and the semantic information transmission quality of the user . T o solve this problem, we propose a prioritised sampling assisted twin delayed deep deterministic policy gradient algorithm to jointly determine the transmitted semantic information and the transmit power per time slot without the communications between the server and the jammer . Compared to standard reinforcement learning methods, the propose method uses an additional Q network to estimate Q values such that the agent can select the action with a lower Q value from the two Q networks thus avoiding local optimal action selection and estimation bias of Q values. Simulation results show that the proposed algorithm can improve the privacy and the semantic information transmission quality by up to 77.8% and 14.3% compared to the traditional reinforcement learning methods. Current communication techniques (e.g., reflected intelligent surface [1], non-terrestrial communications [2], and integrated aerial-ground networks [3]) may not be able to support emerging wireless applications, especially those AI-enabled services, e.g., automatic driving, digital twins, and Metaverse, that require to reliably and efficiently transmit massive volumes of image data that collected by dense visual devices [4]- [6]. Semantic communication [7]-[12] is a novel and promis-W .

Graph-based learning provides a powerful framework for modeling complex relational structures; however, its application within the domain of wireless security remains significantly underexplored. In this work, we introduce the first application of graph-based learning for jamming source localization, addressing the imminent threat of jamming attacks in wireless networks. Unlike geometric optimization techniques that struggle under environmental uncertainties and dense interference, we reformulate the localization as an inductive graph regression task. Our approach integrates structured node representations that encode local and global signal aggregation, ensuring spatial coherence and adaptive signal fusion. To enhance robustness, we incorporate an attention-based \ac{GNN} that adaptively refines neighborhood influence and introduces a confidence-guided estimation mechanism that dynamically balances learned predictions with domain-informed priors. We evaluate our approach under complex \ac{RF} environments with various sampling densities, network topologies, jammer characteristics, and signal propagation conditions, conducting comprehensive ablation studies on graph construction, feature selection, and pooling strategies. Results demonstrate that our novel graph-based learning framework significantly outperforms established localization baselines, particularly in challenging scenarios with sparse and obfuscated signal information. Our code is available at https://github.com/tiiuae/gnn-jamming-source-localization.