Throughput fairness in cognitive backscatter networks with residual hardware impairments and a nonlinear EH model

This paper is to design a throughput fairness-aware resource allocation scheme for a cognitive backscatter network (CBN), where multiple backscatter devices (BDs) take turns to modulate information on the primary signals and backscatter the modulated signals to a cooperative receiver, while harvesting energy to sustain their operations. The nonlinear energy harvesting circuits at the BDs and the residual hardware impairments at the transceivers are considered to better reflect the properties of the practical energy harvesters and transceivers, respectively. To ensure the throughput fairness among BDs, we formulate an optimization problem to maximize the minimum throughput of BDs by jointly optimizing the transmit power of the primary transmitter, the backscattering time and reflection coefficient for each BD, subject to the primary user’s quality of service and BDs’ energy-causality constraints. We introduce the variable slack and decoupling methods to transform the formulated non-convex problem, and propose an iterative algorithm based on the block coordinate descent technique to solve the transformed problem. We also investigate a special CBN with a single BD and derive the optimal solution in the closed form to maximize the BD’s throughput. Numerical results validate the quick convergence of the proposed iterative algorithm and that the proposed scheme ensures much fairness than the existing schemes.