In dense user scenarios, WiFi networks adopting IEEE 802.11n/ac standards often suffer from significant throughput degradation due to increased contention and frequent collisions. Unmanned aerial vehicles (UAVs), with their high mobility, on-demand deployment, and strong line-of-sight communication capabilities, provide a promising solution as supplementary communication infrastructure to offload users from overloaded WiFi access points. This paper investigates a multi-UAV assisted WiFi network architecture, aiming to maximize total network throughput while minimizing the number of deployed UAVs through the joint optimization of user association, UAV coordinates, and power allocation. To address the formulated NP-hard multi-objective optimization problem with dynamic dimensionality, we propose NSGA-II-HLA—a hybrid evolutionary algorithm that integrates a modified non-dominated sorting genetic algorithm II (NSGA-II) for global exploration with a distance-based heuristic for refined user association. Extensive simulation results demonstrate that the proposed approach significantly enhances network throughput, reduces UAV deployment cost, and achieves balanced performance across heterogeneous access domains.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution for aerial surveillance applications, such as traffic monitoring, disaster management, and infrastructure inspection. However, in urban environments, ground-based obstructions frequently disrupt the Line-of-Sight (LoS) links between the unmanned aerial vehicle (UAV) and ground users (GUs), which leads to significant performance degradation in video streaming transmission. To enhance air-to-ground (A2G) communication quality, intelligent reflecting surfaces (IRSs) can be employed to construct reconfigurable UAV- IRS- GU links. In this article, we present a novel framework that integrates an IRS with a multiantenna UAV to enable high-quality aerial video streaming service for a group of GUs simultaneously. By jointly optimizing the UAV trajectory, operation time, transmit beamforming, and phase shifts, the total energy consumption of the UAV is minimized while satisfying Quality of Service (QoS) requirements for video streaming. The problem is formulated as an intractable nonconvex optimization problem with closely coupled variables. To tackle such a challenge, we propose a two-stage algorithm to obtain a suboptimal solution. The first stage focuses on minimizing the UAV’s propulsion energy consumption through path discretization, alternating optimization (AO), and successive convex approximation (SCA) techniques. The second stage focuses on communication energy minimization, achieved through a double-loop iterative algorithm based on the penalty-based block coordinate descent (P-BCD) technique. Simulation results verify the effectiveness of the proposed algorithms and show significant energy savings compared to several baseline schemes.

Non-Orthogonal Multiple Access (NOMA) allows for the sharing of communication link resources among multiple users, which increases spectrum efficiency. In this paper, we consider the NOMA-based mobile edge computing (MEC) networks with unmanned aerial vehicle (UAV), where convenient computation offloading services for ground devices (GDs) with NOMA is provided. The main focus is to investigate the computation capacity in terms of computation throughput, which is characterized by the total size of completed tasks achieved through air-ground collaboration. To guarantee the fairness of GDs, the minimum computation throughput for all GDs is maximized by jointly designing the UAV trajectory, channel relationship coefficient, and computation resource allocation, where the formulated problem is non-convex with closely coupled mixed-integer design variables. A novel penalty based iterative algorithm is proposed, where penalty term is employed to penalize non-integer solution and an inexact block coordinate decent method is adopted to avoid strong locality of the optimized solution, where the convergence is also proved. We conduct extensive simulations and show that our joint design algorithm outperforms other benchmark schemes.

Unmanned aerial vehicle (UAV) is a promising solution to flexibly provide video service for scenarios with temporary traffic. Compared with single-antenna UAV, multi-antenna UAV improves the spectrum efficiency for video transmission. In this paper, we investigate a multi-antenna UAV-enabled streaming system for providing video service to multiple ground users (GUs) simultaneously. To fully utilize the spatial multiplexing gain brought by multiple antennas, we aim to maximize the minimum quality of experience (QoE) for GUs through joint optimization of video playback rate and transmission scheduling as well as UAV trajectory, where the tradeoff between video quality and video playback fluctuation are also taken into account. The optimization problem is formulated as a challenging mixed-integer nonlinear optimization problem. A double-loop iterative algorithm is proposed to obtain a suboptimal solution by employing penalty block coordinate descent (P-BCD) technique. To reduce the influence induced by violation of equality constraint, we update the penalty parameter in the outer loop. In the inner loop, we solve the penalized problem with given penalty parameter through BCD and ConCave-Convex procedure (CCCP) as well as successive convex approximation (SCA) techniques. Simulation results illustrate remarkable performance gains of our proposed scheme compared to benchmarks, which also reveals the tradeoff between quality and playback fluctuation of video.

Utilizing unmanned aerial vehicles (UAVs) as edge caching devices to provide scalable video services to ground users is a promising endeavor, where the caching placement should balance the trade-off between video quality and video diversity. In this paper, we investigate the scalable video coding (SVC)-based layered caching scheme in cache-enabled multi-UAV networks, where specified layers of video are cached at different UAV s. We aim to minimize the aggregate video access delay of all users, via joint design of layered caching placement and UAV deployment as well as user association to provide video services with different qualities. A mixed-integer non-convex optimization problem is formulated which is arduous to solve directly, we decomposed the original problem into two subproblems and proposed corresponding algorithms, i.e., penalty successive convex approximation (P-SCA) based user association optimization algorithm and penalty difference-of-convex (P-DC) programming based UAV deployment and layered caching placement algorithm. An efficient iterative algorithm is proposed wherein we alternatively optimize two subproblems until the algorithm converges. Simulation results show that the proposed algorithm can achieve considerable benefits compared with other benchmark schemes in terms of video access delay.

Mobile edge computing (MEC) has been considered to provide computation services near the edge of mobile networks, while the unmanned aerial vehicle (UAV) is becoming an important integrated component to extend service coverage. In this paper, we consider a UAV-enabled MEC with binary computation offloading, where a UAV serves as an aerial edge server and each task of devices is either executing locally or offloading to the aerial edge server as a whole. To provide fairness among different ground devices, we aim to maximize the minimum computation throughput for all devices via the joint design of computing mode selection and UAV trajectory as well as resource allocation. The optimization problem is formulated as a mixed-integer nonlinear problem consisting of binary variables, which is difficult to tackle. The influence of non-binary solutions is penalized with a penalty function, based on which we develop an efficient iteration algorithm to obtain a suboptimal solution via leveraging the penalty successive convex approximation (P-SCA) method and difference of two convex (D.C.) optimization framework, where the algorithm is guaranteed to converge. Extensive simulations are conducted and the results with different system parameters show the effectiveness of the proposed joint design algorithm compared with other benchmark schemes.