Throughput Maximization Algorithm for Cognitive Backscatter Communication with Imperfect CSI

To improve spectrum transmission efficiency and suppress the effect of channel uncertainties, a throughput-maximization algorithm is proposed for Cognitive Backscatter Communication with imperfect channel state information. Firstly, considering the constraints of the maximum transmit power of the Primary Base Station (PBS), transmission time, user quality of service, and bounded channel uncertainty, a multivariable coupled nonlinear robust throughput-maximization model is formulated by jointly optimizing the PBS's beamforming vector, the reflection coefficient and the transmission time. Then, the original problem is transformed into a convex optimization problem by using the worst-case approach, the S-Procedure, successive convex approximation, alternating optimization, and an iteration-based robust resource allocation algorithm is proposed to solve it. Simulation results show that the proposed algorithm has better throughput and robustness compared with the non-robust algorithm, and the outage probability is reduced by 2.39%.