Hierarchical Optimization of UAV Deployment and Resource Allocation for ISAC-Enabled Low-Altitude Wireless Networks

Sensing for Reliable UAV Communication: Robust Trajectory and Resource Optimization in Low-Altitude Networks

In low-altitude wireless networks, sensing-aided communication has emerged as a promising integrated sensing and communication (ISAC) paradigm for unmanned aerial vehicle (UAV) tracking and communication. This paper investigates reliable sensing-aided communication for multiple cellular-connected UAVs under mobility uncertainties. Specifically, we maximize the minimum outage capacity among UAVs by jointly optimizing their real-time predicted positions, as well as the base station (BS) transmit power and bandwidth allocations. To address the non-convex and intractable maximum tolerable outage probability (OP) constraints, two robust optimization schemes are proposed based on a continuous confidence ellipse (CE) and discretized inverse-whitened sectors (IWSs), respectively. For the CE-based scheme, an efficient algorithm is proposed to optimize the predicted UAV positions individually via block successive convex approximation, followed by convex resource allocation. For the IWS-based scheme, an IWS-based OP approximation is proposed to facilitate the robust optimization, based on which a low-complexity IWS selection method is proposed to decouple the optimization variables. Then, a similar sequential optimization algorithm is proposed based on the projected gradient descent approach. The two algorithms are further unified into a common trajectory-resource optimization framework, revealing a low-complexity structure for robust UAV trajectory and resource management. Simulation results validate the effectiveness of our proposed OP approximation, demonstrate the significant outage capacity improvement of the proposed robust optimization schemes over benchmark schemes, and illustrate the superiority of the IWS-based scheme over the CE-based scheme.

Joint optimization of 3D deployment and power allocation for multi-UAV base stations

In temporary emergency communication coverage scenarios where terrestrial communication infrastructure is damaged or lacks sufficient capacity, UAVs equipped with base stations have emerged as an effective solution due to their flexible deployment and rapid response capability. However, in multi-UAV networks, the three-dimensional deployment of UAVs significantly affects air-to-ground link quality, while power allocation further determines the level of system interference and throughput performance. To address this issue, this paper considers a multi-UAV communication system and jointly takes into account user link reliability and service requirement satisfaction, thereby establishing a joint optimization model for QoS-constrained coverage and network throughput. To address the non-convex joint optimization problem, a problem-tailored dual-population cooperative NSGA-II framework, termed IDPC-NSGA-II, is developed. By coupling dual-population evolution, adaptive mutation, uncovered-user-guided local search, and interference-aware repair with the characteristics of multi-UAV emergency communications, the proposed method improves the trade-off between QoS-constrained coverage and network throughput. Simulation results in a representative emergency communication scenario show that the proposed method achieves a favorable trade-off between QoS-constrained coverage and throughput, and outperforms the compared algorithms under the considered network setting.

Joint Beamforming and Trajectory Optimization Algorithm for RSMA-UAV-Enabled Integrated Sensing and Communication System

Integrated sensing and communication (ISAC) enables concurrent wireless communication and target sensing using shared hardware and spectrum resources, and is regarded as a key technology for next-generation mobile networks. To address the challenge of coordinating sensing and communication in multiuser scenarios, this paper investigates an unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and proposes a joint beamforming and trajectory optimization framework. Specifically, ground users are first clustered subject to a capacity–diameter constraint, and the UAV employs RSMA to serve users within each cluster. The sensing performance is characterized by the composite transmit beampattern gain in the target direction contributed by the common stream and all private streams. Under constraints on users’ downlink rates, transmit power, and sensing quality, we formulate an optimization problem to maximize the average downlink rate. By adopting a block coordinate descent (BCD) framework, the resulting non-convex problem is decomposed into three subproblems, namely cluster scheduling, rate allocation and beamforming, and UAV trajectory optimization. These subproblems are then solved via linear programming, semidefinite relaxation, and successive convex approximation, respectively. Numerical results demonstrate that, while satisfying the sensing-quality requirement, the proposed algorithm significantly improves the achievable system downlink rate.

A Survey on UAV-Assisted Wireless Communications for Future Networks

Unmanned aerial vehicles (UAVs) have emerged as promising aerial platforms for next-generation wireless networks, offering three-dimensional mobility, rapid deployment, and high line-of-sight (LoS) link probability. This paper presents a structured overview of UAV-assisted wireless communications, covering key network architectures, air-to-ground channel characteristics, mobility-aware deployment and trajectory design, resource management, and multi-UAV cooperation. We further review recent integrations of UAVs with emerging technologies such as artificial intelligence (AI)-driven optimization, reconfigurable intelligent surface (RIS), integrated sensing and communication (ISAC), multiple-input multiple-output (MIMO), and semantic communication. Integration scenarios and recent research trends in beyond-5G and 6G networks are discussed, and open challenges along with future research directions are identified. This survey aims to provide a concise yet comprehensive reference for researchers and engineers working on UAV-assisted wireless network design.

Multi-UAV Energy Consumption Minimization for Multilayer Aerial Wireless-Powered MEC: An Online Stochastic Optimization Approach

Joint Beamforming and Trajectory Design for Multi-UAV-Assisted Integrated Sensing, Communication, and Power Transfer Networks

Integrated Sensing, Communication, and Power Transfer (ISCPT) is a key enabler for sixth-generation networks, enhancing resource utilization to support massive low-power devices. However, existing research has predominantly focused on a single Uncrewed Aerial Vehicle (UAV) in communication and power transfer, lacking the capability to facilitate joint sensing and power transfer of multi-UAVs for moving targets considering estimation errors. To tackle the above challenge, we propose for the first time a multi-UAV-assisted ISCPT algorithm serving both multiple Communication Users (CUs) and Energy Receivers (ERs), featuring a sensing-assisted Wireless Power Transfer (WPT) framework. Specifically, we first derive the Fisher information matrix and Cramér–Rao bound for multi-ER positioning under location uncertainty. Then, we formulate a two-phase optimization problem where sensing directly refines location estimation to achieve robust WPT efficiency maximization. To solve the formulated NP-hard problems, we design a two-phase algorithm by jointly optimizing association scheduling with CUs and ERs, beamforming and trajectory design for UAVs, based on generalized Petersen’s sign-definiteness lemma, Lagrangian relaxation and S-procedure. Numerical results validate that the proposed algorithm achieves a maximum WPT efficiency improvement of 42.3% compared with several representative baseline schemes, demonstrating strong practicality for multi-UAV-assisted ISCPT networks.