Performance Analysis of a Backscatter-assisted Full-Duplex Wireless-Powered Cognitive Radio Network

To prolong the lifespan of low-power cognitive sensors, we consider a backscatter-assisted wireless-powered cognitive radio network (WPCRN) in which the sensors must harvest sufficient energy to support conventional data transmission. When the sensors experience low energy levels, they switch to the backscatter communication mode to reduce the latency prior to transmitting sensed data. Additionally, we use the full-duplex model to achieve improved spectral and time efficiency. However, due to channel uncertainty, designing robust conventional and backscatter data transmission is challenging. Thus, the goal is to design a robust data combining vector against channel inaccuracies and, consequently, maximize the sum-throughput of all the sensors. We propose an efficient and low-complexity algorithm named Joint Optimal Time and Energy Allocation with Robust Beamforming (JOTEA-R). Numerical results show that the JOTEA-R algorithm improves the sum-throughput by approximately 15% compared to the benchmark equal time allocation algorithm with robust beamforming. The proposed system model can be used in a remote environmental monitoring network in which the sensors must continuously monitor and transmit information such as temperature, humidity, air quality, and pollution levels data to a cloud data center.