Resource Allocation and Trajectory Planning in Integrated Sensing and Communication Enabled UAV-Assisted Vehicular...

Area of Science:

• Wireless Communication
• Network Engineering
• Signal Processing

Background:

• Vehicular networks face challenges in providing high data rates and reliable sensing services.
• Integrated Sensing and Communication (ISAC) technology offers a promising solution for Unmanned Aerial Vehicle (UAV)-assisted networks.
• Balancing communication and sensing performance in UAV-assisted vehicular networks is crucial for efficient resource utilization.

Purpose of the Study:

• To maximize the average achievable communication rate in a UAV-assisted vehicular network utilizing ISAC technology.
• To address the complex optimization problem involving UAV trajectory, vehicle association, and subchannel allocation under radar sensing constraints.
• To develop an efficient algorithm for joint optimization of communication and sensing in UAV-ISAC systems.

Main Methods:

• Formulated the problem as a Mixed-Integer Nonlinear Program (MINLP) considering communication and sensing performance trade-offs.
• Proposed an iterative algorithm based on Block Coordinate Descent (BCD) to decompose the MINLP into solvable subproblems.
• Employed Successive Convex Approximation (SCA) and convex optimization techniques to solve the subproblems iteratively.

Main Results:

• The proposed BCD-based algorithm effectively optimizes UAV trajectory, vehicle association, and subchannel allocation.
• Simulation results demonstrate superior average achievable rate performance compared to conventional methods.
• The algorithm successfully balances communication and sensing performance under radar sensing constraints.

Conclusions:

• The developed iterative algorithm provides an effective solution for maximizing average achievable rates in UAV-assisted ISAC vehicular networks.
• The proposed approach demonstrates significant performance gains over existing methods while adhering to sensing requirements.
• This research contributes to the advancement of efficient resource management in future integrated sensing and communication systems.