Conventional federated learning (FL) methods face critical challenges in realistic wireless edge networks, where training data are often limited and heterogeneous, which can lead to unstable training and poor generalization. To address these challenges in a principled manner, we propose a novel Bayesian wireless FL framework grounded in Bayesian inference. By explicitly modeling uncertainty, the proposed framework mitigates local overfitting and client drift, thereby enabling more reliable inference. Nevertheless, adopting Bayesian FL increases communication overhead due to the need to transmit richer model information and fundamentally changes the aggregation process beyond simple averaging. To overcome this limitation, we design a dedicated over-the-air computation (AirComp) scheme tailored to Bayesian FL, which efficiently aggregates local posterior distributions at the distribution level by exploiting the superposition property of wireless channels. Simulations validate the proposed framework, demonstrating significant gains in test accuracy over conventional FL methods, particularly in data-scarce and heterogeneous environments.
Jun-Pyo Hong, Hyowoon Seo, Kisong Lee· International Conference on...· 0 citations
In multiuser unmanned aerial vehicle (UAV)-assisted downlink communications, UAV placement and transmit beamforming are inherently coupled through the propagation geometry. However, fully joint design based on instantaneous channel state information (CSI) is impractical, because the small-scale fading depends on the UAV location to be optimized and thus is unavailable a priori. Moreover, existing joint placement and beamforming methods do not explicitly optimize the UAV position with respect to the geometry-dependent multiuser interference induced by inter-user steering correlation. To address this issue, we propose a correlation-aware geometric framework for joint UAV placement and beamforming. Specifically, the UAV position is first optimized based on long-term channel statistics, where the steering-vector correlation is incorporated into the placement design through a conservative Gaussian surrogate that avoids interference underestimation. The resulting nonconvex positioning problem is then handled using successive convex approximation, auxiliary-variable decoupling, and quadratic transform techniques. For the obtained UAV location, the transmit beamformer is then optimized using instantaneous CSI. Simulation results show that the proposed framework significantly improves the minimum user spectral efficiency by enhancing angular separability among users and reducing inter-user interference. These results demonstrate that UAV placement should be designed not only for desired-link enhancement but also for interference mitigation through geometry-aware user separation.
This study investigates the optimization of three-dimensional (3D) trajectory planning and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless networks with no-fly zones (NFZs) using a deep learning framework. The objective is to maximize the minimum average spectral efficiency (SE) among mobile users served by multiple UAVs while addressing key challenges, including interference from concurrent UAV transmissions, collision avoidance, and NFZ constraints. A realistic probabilistic channel model is considered, where the likelihood of a line-of-sight (LoS) condition is modeled as a function of the elevation angle in the air-to-ground (A2G) link. To solve the formulated optimization problem, a novel deep learning framework with specialized deep neural network (DNN) structures is developed. This framework jointly optimizes 3D UAV trajectory planning and resource allocation, employing an unsupervised learning-based training approach that eliminates the need for labeled data. Performance evaluations demonstrate that the proposed scheme effectively accounts for the probabilistic channel model and co-channel interference while accounting for collision avoidance and NFZ-related constraints. Moreover, it outperforms baseline methods by achieving a higher minimum average SE with real-time computational efficiency, making it practical for UAV-assisted wireless networks.
This paper addresses the self-sustainable operation of unmanned aerial vehicle (UAV) swarms in dense urban 6G networks, where both communication reliability and energy replenishment are strongly affected by building-induced blockage. Although solar harvesting and laser wireless power transfer (WPT) can extend UAV operation, they are tightly coupled with UAV mobility: a communication-favorable position may not be feasible for laser charging, while a charging-oriented position may degrade ground node (GN) service. To capture this coupling, we develop a blockage-aware self-sustaining UAV swarm framework that integrates communication, solar harvesting, safety-compliant laser WPT, and UAV mobility under urban blockage. In the proposed model, buildings serve as common geometric constraints that determine both UAV–GN line-of-sight (LoS) connectivity and laser charging feasibility. We formulate a joint optimization problem of user association, UAV trajectory, laser charging decisions, and battery states to maximize the minimum spectral efficiency among GNs while ensuring sustainable energy operation. To address the resulting nonconvex mixed-integer nonlinear problem, we develop a tailored convexification framework for the coupled communication–charging–mobility design. Simulation results reveal that UAVs adapt their mobility according to solar availability: they prioritize short-range LoS communication when solar energy is sufficient, while moving toward safety-compliant and blockage-free laser charging regions under limited solar harvesting. These results highlight the need for joint mobility control that balances communication service and energy replenishment under building-induced blockage.