Optimization of Stacked Intelligent Metasurfaces Assisted Multi-Device Symbiotic Radio Network

Symbiotic radio (SR) is a promising technique that deals with the challenge of high spectrum and energy consumption posed by the massive connections of internet-of-things (IoT) devices. Although the SR allows the IoT devices currently share the spectrum and energy of the ambient signal, their performance suffer from large attenuation caused by the backscatter transmission. To tackle this problem, in this paper, we propose to deploy stacked intelligent metasurface (SIM) at each of the IoT devices in the SR network, where SIM can increase multi-antenna gain through a stacked architecture. A problem is formulated to jointly optimize the time allocation, the beamforming at the ambient base station, and the phase shift at each SIM, aiming to maximize the sum rate of the IoT devices while ensuring the transmission rate of the ambient receiver. This non-convex problem is solved by a proposed alternating optimization algorithm that uses semidefinite relaxation and Gaussian randomization technique. Simulation results show that the proposed algorithm reaches a peak sum rate of the IoT devices when the SIM has six layers. It achieves a 120% improvement compared to a single-layer SIM, and significantly outperforms the benchmarks. These demonstrate that the multi-layer structure of the SIM can effectively enhance the performance of the SR transmission.