Aug 2026· Italian National Conference on Sensors· 0 citations· 23 references
TL;DR
An unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and a joint beamforming and trajectory optimization framework is investigated and results demonstrate that the proposed algorithm significantly improves the achievable system downlink rate.
Abstract
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.
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.
Zhaolong Ning, Lingfei Li, Xiaojie Wang et al.· IEEE Journal on Selected Are...· 1 citation
In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user. This architecture is practically attractive for future wireless networks due to the UAV's controllable mobility and adaptive sensing coverage in wireless environments. The sensing performance is characterized by the average Cram\'er-Rao bound (CRB), which quantifies the minimum variance of the unbiased angle-of-arrival estimation. To enhance the sensing performance, the UAV trajectory and beamforming parameters are jointly optimized under power and mobility constraints, while satisfying communication requirements to the downlink user. To address the resulting non-convex problem, we employ null-space projection for beamforming design and adopt deep reinforcement learning for the trajectory optimization over a discrete-time scale. In each time slot, beamforming is optimized based on the channel state information to improve CRB performance while mitigating interference between the BS and the communication user. Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.
Simulation results validate the effectiveness of the 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.
Yifan Jiang, Qing-Bin Wu, Hongxun Hui et al.· 0 citations
Driven by the vision of a thriving low-altitude economy and aiming to provide on-demand services for diverse entities, this paper investigates an integrated sensing and communication (ISAC)-enabled low-altitude wireless network (LAWN). Benefiting from flexible mobility and cost-effective cooperative deployment, multiple ISAC-enabled uncrewed aerial vehicles (UAVs) are emerging as an ISAC paradigm for on-demand deployment in LAWN. However, due to the complex inter-UAV interference and resource coupling in LAWN, it is difficult to properly coordinate different constrained resources, including spatial deployment, energy, and wireless channels, to simultaneously meet the sensing and communication requirements. To address these challenges, this paper formulates a sensing–communication optimization (SCO) problem in LAWN by jointly optimizing subcarrier allocation, transmit power allocation, and three-dimensional (3D) UAV deployments to maximize network utility while satisfying quality of service (QoS) requirements for multiple users and target sensing mutual information (MI) requirements. To enable efficient solutions, we propose a hierarchical optimization approach that vertically decouples the SCO problem into two subproblems: a top level employing a Gibbs Sampling–based multi-UAV 3D deployment algorithm for efficient exploration and deployment optimization, and a bottom level performing resource allocation via a dual-based joint power and subcarrier allocation algorithm. Simulation results demonstrate that the proposed approach achieves a favorable trade-off between communication and sensing and significantly enhances the overall performance and adaptability of the LAWN.
Cheng Ma, Zewei Jing, Qinghai Yang et al.· IEEE Transactions on Wireles...· 0 citations
This letter investigates resource allocation for a generalized coordinated direct and relay transmission (CDRT) framework assisted by an amplify-and-forward unmanned aerial vehicle (UAV), where the considered UAV simultaneously forwards the base station’s signals and delivers its own local information, yielding a unified transmission architecture well aligned with practical sensing and control applications. To manage the highly coupled interference topology among direct, forwarded, and relay-originated streams, we adopt the rate-splitting multiple access (RSMA) strategy. We formulate a joint optimization of precoding vectors, the UAV amplification matrix, and rate-splitting parameters to maximize the system sum rate. A penalty-based alternating optimization algorithm that combines semidefinite relaxation and successive convex approximation is customized to tackle the intertwined non-convex fractional terms. Simulation results demonstrate substantial sum-rate gains over the pure-forwarding, NOMA, and SDMA schemes in most considered cases, with the advantage becoming more pronounced under full-load and moderately overloaded conditions.
Dan Jiang, Yuanyuan Gao, Qiao Su et al.· IEEE Wireless Communications...· 0 citations
To meet the collaborative requirements of high-precision communication, sensing, and navigation for unmanned aerial vehicles (UAVs) and other aerial mobile terminals operating in urban low-altitude airspace, this paper proposes an integrated communication-sensing-navigation (ICSN) beamforming design tailored to low-altitude scenarios. In the developed ICSN system model, communication performance is evaluated by the total communication rate of users, while sensing performance is quantified by the Cramér–Rao Bound (CRB) of target angle estimation, enabling accurate positioning and continuous perception for aerial users. A two-stage beamforming strategy is adopted: in the first stage, a wide beam is constructed under integrated sidelobe to mainlobe ratio (ISMR) constraints to sense potential target directions; in the second stage, a weighted optimization problem is formulated to jointly enhance communication rate and angle estimation accuracy, subject to user signal-to-interference-plus-noise ratio (SINR) requirements and mainlobe/sidelobe constraints. To efficiently solve the resulting non-convex problem, this paper proposes an algorithmic framework that combines fractional programming (FP) and alternating optimization (AO), fully utilizing the perception results from the previous round to perform feedback updates for the next round of beam design. Simulation results verify the effectiveness of the proposed method in multi-user and multi-target urban low-altitude scenarios, demonstrating the role of the first-stage directional beam design in system robustness and accuracy. Moreover, the proposed framework facilitates spectrum and hardware reuse across functionalities, thereby contributing to the design of sustainable and energy-efficient aerial communication systems aligned with the principles of green communication.
Ruoyu Lu, Yuexia Zhang, Chuanjun Li et al.· IEEE Transactions on Green C...· 0 citations