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

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This study optimizes unmanned aerial vehicle (UAV) networks for better communication rates under radar sensing limits. The proposed algorithm enhances average achievable rates by balancing communication and sensing performance.

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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.