RIS-Enhanced Cognitive BackCom Networks: Robust Resource Allocation and Passive Beamforming Design

Cognitive backscatter communication (BackCom) is a promising technology for improving the spectrum- and energy-efficiency of Internet of Things by enabling spectrum sharing and energy saving. However, the performance of cognitive BackCom networks is adversely affected by the mutual interference between the primary and secondary systems and the blocked links caused by obstacles. Additionally, assuming perfect channel state information (CSI) is unrealistic in practical cognitive BackCom networks due to the limited signal processing capabilities of cognitive backscatter nodes (CBNs) and channel delays. To address these challenges, we investigate a robust radio resource allocation and passive beamforming problem for a downlink reconfigurable intelligent surface (RIS)-enhanced cognitive BackCom network under the nonlinear energy-harvesting (EH) model and imperfect CSI. In particular, a primary base station serves multiple primary users (PUs), while multiple pairs of CBNs share the spectrum of PUs to communicate with each other in a harvest-then-transmit way. Our goal is to maximize the total energy efficiency (EE) of CBNs subject to the constraints of maximum interference power, minimum EH, time allocation, and the phase shift of the RIS. To solve the nonconvex optimization problem, we propose an iteration-based EE optimization algorithm that leverages methods of quadratic transform, variable substitution, and semidefinite relaxation. Simulation results verify that the proposed algorithm has improved its EE by 11.39% and reduced outage probabilities by 15% compared to the existing algorithms.