Throughput fairness in backscatter-assisted cognitive radio networks with short packets

This work studies the throughput fairness among multiple Internet of Things (IoT) nodes in a backscatter assisted cognitive radio network, where the primary transmitter conveys its long-packet information to its receiver while multiple IoT nodes alternate backscattering their short-packet information to the information receiver via backscatter communication (BackCom). Specifically, we devise a non-convex problem aimed at ensuring the throughput fairness among IoT nodes concerning their transmitted data bits, by jointly optimizing the short-packet blocklength, packet error rate, and power reflection coefficient of each IoT node. Employing the block-coordinated-decent (BCD), the original problem is decoupled into two subproblems, both of which are solved by the proposed golden section based iterative algorithm and the proposed successive convex approximation (SCA) based iterative algorithm, respectively. Then a BCD based iterative algorithm is developed to solve the original problem. Simulations demonstrate the rapid convergence and superiority of the proposed algorithm over several baseline schemes in achieving the fairness of transmission bits.