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.
Abstract
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.
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.
Shunxuan Wang, Qi Zhu· Italian National Conference...· 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
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
Integrated sensing and communication (ISAC) is a rising technology in the next wireless communication networks, enabling the simultaneous execution of communication and sensing tasks by fully utilizing limited spectrum resources. In this work, we investigate the secrecy performance of a dual-uncrewed aerial vehicle (UAV)-assisted secure ISAC system. Specifically, a base station UAV communicates with users and transmits radar signals to locate potential eavesdroppers, while simultaneously providing information to a jammer UAV to perform jamming tasks. Considering constraints such as maximum UAV velocity, transmit power, propulsion energy, and sensing thresholds, we maximize the average secrecy rate by optimizing user scheduling strategies, time allocation, transmit power, and UAV trajectories. The presence of a non-convex problem, originating from tightly coupled variables, is tackled by an efficient iterative algorithm. In particular, the original optimization problem is decomposed into six subproblems, and non-convex subproblems are transformed into approximately convex forms via successive convex approximation. Then, block coordinate descent techniques are employed to solve all subproblems sequentially. Numerical results demonstrate the convergence and effectiveness of the proposed algorithm.
Hongjiang Lei, Jianshuo Geng, Ki-Hong Park et al.· 0 citations
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.
The rapid proliferation of Uncrewed Aerial Vehicles (UAVs) introduces significant challenges to low-altitude airspace security, particularly from unauthorized intrusions. To address these vulnerabilities, Integrated Sensing and Communication (ISAC) has emerged as a key enabler for anti-UAV systems. However, existing studies focusing on cellular networks with fixed base stations are ill-suited for the continuous movement of target UAVs, thus failing to meet the dual demands of flexible sensing and reliable positioning. To address this, we propose an ISAC-enabled anti-UAV scheme solely based on cooperative UAVs. Specifically, we first derive the optimal transmit power under the constraint of space-air transmission outage probability tolerance. Subsequently, we deduce the sensing Fisher information matrix and Cramér-Rao Bound (CRB) by incorporating the position uncertainty of the target UAV. Then, we formulate a long-term CRB minimization problem to enhance cooperative sensing performance. To tackle this NP-hard problem, we design a robust optimization algorithm that jointly optimizes transmit-receive beamforming, association scheduling, and UAV trajectory, by transforming the structurally complex CRB matrix into a set of semi-definite constraints, and resolving the inherent position uncertainty. Numerical results demonstrate that our proposed algorithm outperforms representative algorithms in terms of sensing accuracy and robustness.
Xiaojie Wang, Lingfei Li, Zhaolong Ning et al.· IEEE Transactions on Wireles...· 1 citation