Temporal-Assisted Beamforming and Trajectory Prediction in Sensing-Enabled UAV Communications

TL;DR

This work addresses the challenge of high-mobility UAV communications by integrating sensing and communication through a temporal-assisted beamforming paradigm. It introduces a UAV-centric ISAC framework featuring an omnidirectional sensing phase followed by directional beamforming, coupled with an EKF-based motion estimation that fuses radar echoes and communication data. To solve the resulting non-convex beamforming problem, the authors apply SCA with SDR and refine solutions via Iterative Rank Minimization, achieving significant gains over pilot-based approaches (e.g., up to 156% in multi-object and 136% in single-object scenarios). The proposed framework enhances sensing accuracy and reduces pilot overhead, enabling robust, high-rate UAV links in dynamic multi-target environments and offering practical impact for emergency connectivity and V2X deployments. The results underscore the potential of temporal-assisted ISAC to jointly optimize radar sensing and communications in 3D UAV platforms with scalable antenna arrays.

Abstract

In the evolving landscape of high-speed communication, the shift from traditional pilot-based methods to a Sensing-Oriented Approach (SOA) is anticipated to gain momentum. This paper delves into the development of an innovative Integrated Sensing and Communication (ISAC) framework, specifically tailored for beamforming and trajectory prediction processes. Central to this research is the exploration of an Unmanned Aerial Vehicle (UAV)-enabled communication system, which seamlessly integrates ISAC technology. This integration underscores the synergistic interplay between sensing and communication capabilities. The proposed system initially deploys omnidirectional beams for the sensing-focused phase, subsequently transitioning to directional beams for precise object tracking. This process incorporates an Extended Kalman Filtering (EKF) methodology for the accurate estimation and prediction of object states. A novel frame structure is introduced, employing historical sensing data to optimize beamforming in real-time for subsequent time slots, a strategy we refer to as 'temporal-assisted' beamforming. To refine the temporal-assisted beamforming technique, we employ Successive Convex Approximation (SCA) in tandem with Iterative Rank Minimization (IRM), yielding high-quality suboptimal solutions. Comparative analysis with conventional pilot-based systems reveals that our approach yields a substantial improvement of 156\% in multi-object scenarios and 136\% in single-object scenarios.

Figures (14)

• Figure 1: Sensing-enabled UAV system model.
• Figure 2: Frame structure.
• Figure 3: Evolution model.
• Figure 4: Validation of approximation in (\ref{['approx_a']}) and (\ref{['approx_d']}).
• Figure 5: Comparison of communication performance between the proposed scheme and pilot-based scheme in multi-object and single-object scenarios. For UAV, $h=100$m, $v^u=15$m/s. For multiple, one with $\theta_0 = 75^\circ$, $v_0 = 30$m/s, and $a = -5$m/s², and the other with $\theta_0 = 135^\circ$, $v_0 = -3$m/s, and $a = 1$m/s². For single, $\theta_0 = 60^\circ$, $v_0 = 30$m/s, and $a = -5$m/s².