Vehicular Connectivity on Complex Trajectories: Roadway-Geometry Aware ISAC Beam-tracking

TL;DR

This paper introduces a roadway-geometry-aware ISAC beam-tracking method for vehicles on complex roads, improving tracking accuracy and communication QoS by modeling road geometry and optimizing beamwidth dynamically.

Contribution

It proposes a novel CCS-based IMM-EKF framework and dynamic beamwidth optimization to enhance vehicle tracking and communication in complex roadway environments.

Findings

Outperforms conventional ISAC beam-tracking methods in accuracy

Effective dynamic beamwidth adjustment improves performance

Numerical results validate the proposed approach's superiority

Abstract

In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling.Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication quality of service (QoS) in vehicle to infrastructure (V2I) networks, it is essential to model the complicated roadway geometry. To that end, we impose the curvilinear coordinate system (CCS) in an interacting multiple model extended Kalman filter (IMM-EKF) framework. By doing so, both the position and the motion of the vehicle on a complicated road can be explicitly modeled and precisely tracked attributing to the benefits from the CCS. Furthermore, an optimization problem is formulated to maximize the array gain through dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account.Numerical simulations demonstrate that the roadway geometry-aware ISAC beamforming approach outperforms the communication-only based and ISAC kinematic-only based technique in the tracking performance. Moreover, the effectiveness of the dynamic beamwidth design is also verified by our numerical results.