Secondary Network Capacity Optimization for IRS-and WPT-Assisted Symbiotic Radio Systems

Symbiotic Radio (SR) presents an innovative wireless paradigm that simultaneously supports active primary and passive secondary transmissions. This technology significantly enhances spectrum and energy efficiency in network scenarios that support data transmission from a large number of Internet-of-Things (IoT) devices. Nonetheless, the received backscatter signal experiences attenuation due to the double path loss effect, thereby constraining the secondary network’s capacity to satisfy the data transmission requirements of IoT applications. To enhance the secondary network capacity with high energy efficiency in SR systems, we synergistically apply two promising technologies–Wireless Power Transmission (WPT) and Intelligent Reflecting Surfaces (IRS). Accordingly, this paper explores the optimization of secondary network capacity in an SR system assisted by IRS and WPT, where high-density devices are organized into clusters. We adopt a hybrid access method that integrates Time Division Multiple Access (TDMA) for clusters accessing the Base Station (BS) and Non-Orthogonal Multiple Access (NOMA) for Backscatter Devices (BDs) communicating with each other in a cluster. By jointly optimizing active beamforming at the BS, passive beamforming at the IRS, and hybrid transmission time allocation, we maximize the sum data rate of the secondary links while ensuring that the communication requirements of primary links are met. To tackle this complex, high-dimensional, nonlinear problem, we propose a capacity optimization algorithm based on Deep Reinforcement Learning (DRL). We conduct system performance evaluations, and the results validate the advantages of our proposed scheme in optimizing the secondary network capacity of SR systems compared to alternative approaches.