Throughput Maximization for Backscatter Communication in Cell-Free Symbiotic Radio Networks With Hybrid CSR-PSR

With the evolution of sixth generation (6G) technologies and Internet of Things (IoT), base stations and IoT devices are deployed densely to achieve the ultra-high data rate, resulting in the scarcity of spectrum resource. To tackle it, we study a cell-free symbiotic radio network (CF-SRN) that consists of the cell-free network (CFN) and IoT network, and includes multiple access points (APs), multiple backscatter devices (BDs), and a single receiver. APs collaboratively transmit primary radio frequency (RF) signals to the receiver, and BDs split the energy of primary RF signals to perform backscatter communication, and energy harvesting. Existing works focus on the SRN with commensal symbiotic radio (CSR) or parasitic symbiotic radio (PSR) setup, while we design a hybrid CSR-PSR setup to balance the tradeoff between primary communication and backscatter communication in the CF-SRN. Based on the design, we formulate the sum backscatter throughput maximization problem by optimizing the time allocation vector, beamforming vectors of APs and BDs, and reflection coefficients of BDs, subject to the minimum sum primary throughput constraint. Due to the coupling relationship among high-dimensional variables, we decompose the formulated problem into time allocation optimization (TAO) subproblem, beamforming optimization (BO) subproblem, and reflection coefficient optimization (RCO) subproblem. For TAO subproblem, we use a linear programming method to obtain the optimal solution. For BO subproblem and RCO subproblem, we propose a block coordinate descent-based semi-definite relaxation and successive convex approximation (BSS) algorithm. Simulation results validate the superiority of the BSS algorithm and hybrid CSR-PSR setup.