Secure Transmission for Movable Antennas Empowered Cell-Free Symbiotic Radio Communications

TL;DR

This paper introduces a movable antenna-based secure transmission scheme for cell-free symbiotic radio systems, optimizing antenna positions and beamforming to enhance secrecy rates against eavesdroppers.

Contribution

It proposes a novel movable antenna approach combined with joint optimization techniques to improve physical layer security in symbiotic radio networks.

Findings

Movable antennas significantly improve secrecy rates.

The proposed optimization framework effectively enhances security.

Numerical results validate the benefits of movable antennas and the optimization methods.

Abstract

In this paper, a novel movable antenna (MA) empowered secure transmission scheme is designed for cell-free symbiotic radio (SR) systems in the presence of an eavesdropper (Eve). Specifically, multiple distributed access points (APs) equipped with MAs collaboratively transmit confidential information to the primary user (PU), in the meanwhile the backscatter device (BD) transmits its own information to the secondary user (SU) by reflecting incident signals from the APs. The MAs deployed at the APs can adjust their positions flexibly to improve channel conditions between the APs and the PU/SU/BD and suppress the eavesdropping from the Eve on confidential information at the PU. Under this setup, we maximize the secrecy rate of primary transmission through jointly optimizing the APs' transmission beamforming vectors and the positions of the MAs, while adhering to the quality of service constraints at the SU. To address the challenges caused by the non-convexity and find a near-optimal solution, an alternating optimization (AO) framework is proposed, utilizing the successive convex approximation method, the semi-definite relaxation technology and a genetic algorithm modified particle swarm optimization (GA-PSO) algorithm. Numerical results demonstrate the secrecy rate enhancement provided by utilizing the MAs and show the impact of the GA-PSO algorithm for improving the solving accuracy.