基于传输公平性的多无人机通感一体化空间部署与波束成形设计

Objective: As economic and social development rapidly progresses, the demand for applications across various sectors is increasing. The use of higher frequency bands for future 6G communication is advancing to facilitate enhanced perception. Additionally, due to the inherent similarities in signal processing and hardware configurations between sensing and communication, Integrated Sensing And Communication (ISAC) is becoming a vital area of research for future technological advancements. However, during sudden emergencies, communication coverage and target detection in rural and remote areas with limited infrastructure face considerable challenges. The integration of communication and sensing in Unmanned Aerial Vehicles (UAVs) presents a unique opportunity for equipment flexibility and substantial research potential. Despite this, current academic research primarily focuses on single UAV systems, often prioritizing communication rates while neglecting fairness in multi-user environments. Furthermore, existing literature on multiple UAV systems has not sufficiently addressed the variations in user or target numbers and their random distributions, which impedes the system’s capability to adaptively allocate resources based on actual demands and improve overall efficiency. Therefore, exploring the application of integrated communication and sensing technologies within multi-UAV systems to provide essential services to ground-based random terminals holds significant practical importance. Methods: This paper addresses the scenario in which ground users and sensing targets are randomly distributed within clusters. The primary focus is on the spatial deployment of UAVs and their beamforming techniques tailored for ground-based equipment. The system seeks to enhance the lower bound of user transmission achievable rates by optimizing the communication and sensing beamforming variables of the UAVs, while also adhering to essential communication and sensing requirements. Key constraints considered include the aerial-ground coverage correlation strategy, UAV transmission power, collision avoidance distances, and the spatial deployment locations. To effectively address the non-convex optimization problem, the study divides it into two sub-problems: the joint optimization of aerial-ground correlation and planar position deployment, and the joint optimization of communication and sensing beamforming. The first sub-problem is solved using an improved Mean Shift algorithm (MS), which focuses on optimizing aerial-ground correlation variables alongside UAV planar coordinate variables (Algorithm 1). The second sub-problem employs a quadratic transformation technique to optimize communication beamforming variables (Algorithm 2), further utilizing a successive convex approximation strategy to tackle the optimization challenges associated with sensing beamforming (Algorithm 3). Ultimately, a Block Coordinate Descent algorithm is implemented to alternately optimize the two sets of variables (Algorithm 4), leading to a relatively optimal solution for the system. Results and Discussions: Algorithm 1 focuses on establishing aerial-ground correlations and determining the planar deployment of UAVs. During the clustering phase, users and targets are treated as equivalent sample entities, with ground sample points generated through a Poisson clustering random process. These points are subsequently categorized into nine optimal clusters using an enhanced mean shift algorithm. Samples within the same Voronoi category are assigned to a single UAV, positioned at the mean shift center for optimal service coverage. Algorithm 4 addresses the beamforming requirements for UAVs servicing ground users or targets. Remarkably, multiple UAVs achieve convergence within seven iterations concerning regional convergence. The dynamic interplay between communication and sensing resources is highlighted by variations in the number of sensing targets and the altitude of UAV deployment. The fairness-first approach proposed in this paper, in contrast to a rate-centric strategy, ensures maximum individual transmission quality while maintaining balanced system performance. Furthermore, the overall scheme, referred to as MS+BCD, is compared with two benchmark algorithms: Block Coordinate Descent beamforming optimization with Central point Sensing Deployment (CSD+BCD) and Random Sensing Beamforming with Mean Shift deployment (MS+RSB). The proposed optimization strategy clearly demonstrates advantages in system effectiveness, irrespective of changes in beam pattern gain or increases in UAV antenna numbers. Conclusions: This paper addresses the multi-UAV coverage challenge within the framework of Integrated Sensing and Communication. With a focus on equitable user transmission rates, this study incorporates constraints related to communication and sensing power, beam pattern gain, and aerial-ground correlation. By employing an enhanced Mean Shift algorithm along with the Block Coordinate Descent method, this research optimizes a variety of parameters, including aerial-ground correlation strategies, UAV planar deployment, and communication-sensing beamforming. The objective is to maximize the system’s transmission rate while ensuring high-quality user transmission and fair resource allocation, thereby providing a novel approach for multi-UAV systems enhanced by integrated communication and sensing. Future research will extend these findings to tackle additional altitude optimization challenges and to ensure equitable resource distribution across different UAV coverage zones.