Intelligent Surface Empowered Sensing and Communication: A Novel Mutual Assistance Design

TL;DR

This paper introduces a mutual assistance scheme using intelligent surfaces on vehicles to enhance sensing and communication performance in vehicular networks, addressing weak echo signals for precise positioning.

Contribution

It proposes a novel mutual assistance design for intelligent surface-mounted vehicles, optimizing sensing and communication integration with a new algorithm and theoretical analysis.

Findings

Performance trade-off bound of sensing and communication is significantly improved.

The proposed algorithm achieves optimal time allocation for maximizing communication rate under sensing constraints.

Simulation results confirm the effectiveness of mutual assistance in intelligent surface-aided vehicular networks.

Abstract

Integrated sensing and communication (ISAC) is a promising paradigm to provide both sensing and communication (S&C) services in vehicular networks. However, the power of echo signals reflected from vehicles may be too weak to be used for future precise positioning, due to the practically small radar cross section of vehicles with random reflection/scattering coefficient. To tackle this issue, we propose a novel mutual assistance scheme for intelligent surface-mounted vehicles, where S&C are innovatively designed to assist each other for achieving an efficient win-win integration, i.e., sensing-assisted phase shift design and communication-assisted high-precision sensing. Specifically, we first derive closed-form expressions of the echo power and achievable rate under uncertain angle information. Then, the communication rate is maximized while satisfying sensing requirements, which is proved to be a monotonic optimization problem on time allocation. Furthermore, we unveil the feasible condition of the problem and propose a polyblock-based optimal algorithm. Simulation results validate that the performance trade-off bound of S&C is significantly enlarged by the novel design exploiting mutual assistance in intelligent surface-aided vehicular networks.