Ambient Backscatter: A New Approach to Improve Network Performance for RF-Powered Cognitive Radio Networks

作者：Dinh Thai Hoang; Niyato Dusit; Wang Ping; Kim Dong In^*; Han Zhu

来源：IEEE Transactions on Communications, 2017, 65(9): 3659-3674.

DOI：10.1109/TCOMM.2017.2710338

• Cognitive radios
• ambient backscatter
• RF energy harvesting
• convex optimization

This paper introduces a new solution to improve the performance for secondary systems in radio frequency (RF) powered cognitive radio networks (CRNs). In a conventional RF-powered CRN, the secondary system works based on the harvest-then-transmit protocol. That is, the secondary transmitter (ST) harvests energy from primary signals and then uses the harvested energy to transmit data to its secondary receiver (SR). However, with this protocol, the performance of the secondary system is much dependent on the amount of harvested energy as well as the primary channel activity, e.g., idle and busy periods. Recently, ambient backscatter communication has been introduced, which enables the ST to transmit data to the SR by backscattering ambient signals. Therefore, it is potential to be adopted in the RF-powered CRN. We investigate the performance of RF-powered CRNs with ambient backscatter communication over two scenarios, i.e., overlay and underlay CRNs. For each scenario, we formulate and solve the optimization problem to maximize the overall transmission rate of the secondary system. Numerical results show that by incorporating such two techniques, the performance of the secondary system can be improved significantly compared with the case when the ST performs either harvest-then-transmit or ambient backscatter technique.

• 出版日期2017-9
• 单位南阳理工学院