Integrated Sensing, Computing and Semantic Communication for Vehicular Networks

TL;DR

This paper proposes an integrated framework for sensing, computing, and semantic communication in vehicular networks, enhancing security, accuracy, and data throughput through joint optimization and advanced filtering techniques.

Contribution

It introduces a novel ISCSC framework with secure communication, vehicle tracking via EKF, and a joint optimization approach for maximizing secrecy rate and minimizing estimation error.

Findings

Effective vehicle tracking with EKF

Enhanced secure semantic communication

Improved sensing accuracy and data throughput

Abstract

This paper introduces a novel framework for integrated sensing, computing, and semantic communication (ISCSC) within vehicular networks comprising a roadside unit (RSU) and multiple autonomous vehicles. Both the RSU and the vehicles are equipped with local knowledge bases to facilitate semantic communication. The framework incorporates a secure communication design to ensure that messages intended for specific vehicles are protected against interception. In this model, an extended Kalman filter (EKF) is employed by the RSU to accurately track all vehicles. We formulate a joint optimization problem that balances maximizing the probabilistically constrained semantic secrecy rate for each vehicle while minimizing the sum of the posterior Cram\'er-Rao bound (PCRB), subject to the RSU's computing capabilities. This non-convex optimization problem is addressed using Bernstein-type inequality (BTI) and alternating optimization (AO) techniques. Simulation results validate the effectiveness of the proposed framework, demonstrating its advantages in reliable sensing, high data throughput, and secure communication.