arXiv subjects

Kisong Lee

Publications and source records attributed to Kisong Lee.

6 recordsLinked to original sources

arXiv2026

Rethinking Joint UAV Placement and Beamforming: A Correlation-Aware Geometric Approach

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.

eess.SY↗

arXiv2026

Beyond Average-Channel-Based Rate Approximations: UAV Trajectory and Scheduling Optimization With Expected Rate Consideration

This paper investigates the joint optimization of trajectory, user scheduling, and time-slot duration in unmanned aerial vehicle (UAV)-assisted wireless communication systems under minimum expected spectral efficiency (SE) constraints. Unlike most existing studies that approximate the expected SE by substituting the random channel gain with its mean value, thereby evaluating the SE at the average channel realization and overestimating the true expected SE due to Jensen's inequality, we approximate the expected SE by numerically integrating the SE over the channel distributions. Specifically, instead of relying on average-channel-based approximations, we develop a conservative yet tractable quadrature-based approximation by discretizing the associated cumulative distribution functions. The resulting finite-sum representation explicitly accounts for the probabilistic LoS structure and channel fading effects, while remaining tractable for optimization. Leveraging this lower bound, we formulate a mission completion time minimization problem subject to minimum expected-SE requirements for all ground nodes. The resulting problem is a mixed-integer nonconvex optimization, which is tackled via a penalty-based block coordinate descent framework. The proposed algorithm alternately optimizes the scheduling decisions and the UAV trajectory along with adaptive time-slot durations, and maintains feasibility with respect to the original expected-SE constraints by leveraging successive convex approximation and quadratic transform techniques. Simulation results demonstrate that the proposed method strictly satisfies the minimum expected-SE constraints and achieves a significantly shorter mission completion time than conventional average-channel-based approaches, which are shown to yield infeasible or overly conservative solutions.

eess.SY↗

arXiv2025

Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity

Federated Learning (FL) has gained considerable attention as a privacy-preserving and localized approach to implementing edge artificial intelligence (AI). However, conventional FL methods face critical challenges in realistic wireless edge networks, where training data is both limited and heterogeneous, often leading to unstable training and poor generalization. To address these challenges, we propose a Bayesian wireless FL framework that captures model uncertainty via posterior distributions and performs distribution-level aggregation, mitigating local overfitting and client drift. However, this formulation increases communication overhead and prevents the direct use of conventional Over-the-Air Computation (AirComp), which is widely used to improve communication efficiency in standard FL. To overcome this, we develop a transmission-compatible reformulation of posterior aggregation that enables distribution-level Bayesian updates to be computed over the air, along with a closed-form distributed transmit power control strategy derived from convergence analysis under practical wireless impairments. Extensive simulations demonstrate that the proposed framework significantly improves test accuracy and calibration performance compared to conventional FL methods, particularly in data-scarce and heterogeneous environments.

eess.SP↗

arXiv2025

Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks

Accurate and real-time three-dimensional (3D) pose estimation is challenging in resource-constrained and dynamic environments owing to its high computational complexity. To address this issue, this study proposes a novel cooperative inference method for real-time 3D human pose estimation in mobile edge computing (MEC) networks. In the proposed method, multiple end devices equipped with lightweight inference models employ dual confidence thresholds to filter ambiguous images. Only the filtered images are offloaded to an edge server with a more powerful inference model for re-evaluation, thereby improving the estimation accuracy under computational and communication constraints. We numerically analyze the performance of the proposed inference method in terms of the inference accuracy and end-to-end delay and formulate a joint optimization problem to derive the optimal confidence thresholds and transmission time for each device, with the objective of minimizing the mean per-joint position error (MPJPE) while satisfying the required end-to-end delay constraint. To solve this problem, we demonstrate that minimizing the MPJPE is equivalent to maximizing the sum of the inference accuracies for all devices, decompose the problem into manageable subproblems, and present a low-complexity optimization algorithm to obtain a near-optimal solution. The experimental results show that a trade-off exists between the MPJPE and end-to-end delay depending on the confidence thresholds. Furthermore, the results confirm that the proposed cooperative inference method achieves a significant reduction in the MPJPE through the optimal selection of confidence thresholds and transmission times, while consistently satisfying the end-to-end delay requirement in various MEC environments.

cs.CV↗

arXiv2025

Blockage-Aware UAV-Assisted Wireless Data Harvesting With Building Avoidance

Unmanned aerial vehicles (UAVs) offer dynamic trajectory control, enabling them to avoid obstacles and establish line-of-sight (LoS) wireless channels with ground nodes (GNs), unlike traditional ground-fixed base stations. This study addresses the joint optimization of scheduling and three-dimensional (3D) trajectory planning for UAV-assisted wireless data harvesting. The objective is to maximize the minimum uplink throughput among GNs while accounting for signal blockages and building avoidance. To achieve this, we first present mathematical models designed to avoid cuboid-shaped buildings and to determine wireless signal blockage by buildings through rigorous mathematical proof. The optimization problem is formulated as nonconvex mixed-integer nonlinear programming and solved using advanced techniques. Specifically, the problem is decomposed into convex subproblems via quadratic transform and successive convex approximation. Building avoidance and signal blockage constraints are incorporated using the separating hyperplane method and an approximated indicator function. These subproblems are then iteratively solved using the block coordinate descent algorithm. Simulation results validate the effectiveness of the proposed approach. The UAV dynamically adjusts its trajectory and scheduling policy to maintain LoS channels with GNs, significantly enhancing network throughput compared to existing schemes. Moreover, the trajectory of the UAV adheres to building avoidance constraints for its continuous trajectory, ensuring uninterrupted operation and compliance with safety requirements.

cs.IT↗

arXiv2023

On Correcting Errors in Existing Mathematical Approaches for UAV Trajectory Design Considering No-Fly-Zones

Motivated by the fact that current mathematical methods for the trajectory design of an unmanned aerial vehicle (UAV) considering no-fly-zones (NFZs) cannot perfectly avoid NFZs throughout the entire continuous trajectory, this study introduces a new constraint that ensures the complete avoidance of NFZs. Moreover, we provide mathematical proof demonstrating that a UAV operating within the proposed constraints will never violate NFZs. Under the proposed constraint on NFZs, we aim to optimize the scheduling, transmit power, length of the time slot, and the trajectory of the UAV to maximize the minimum throughput among ground nodes without violating NFZs. To find the optimal UAV strategy from the non-convex optimization problem formulated here, we use various optimization techniques, in this case quadratic transform, successive convex approximation, and the block coordinate descent algorithm. Simulation results confirm that the proposed constraint prevents NFZs from being violated over the entire trajectory in any scenario. Furthermore, the proposed scheme shows significantly higher throughput than the baseline scheme using the traditional NFZ constraint by achieving a zero outage probability due to NFZ violations.

math.OC↗