Optimal decision making in multi-channel RF-powered cognitive radio networks with ambient backscatter capability

摘要

In RF-powered backscatter cognitive radio networks, the secondary user transmits his data either by harvesting energy then actively transmitting the data or by backscattering. However, it is challenging for the secondary user to determine the optimal licensed channel to select and the optimal action to do under the uncertainty of the state of the licensed channels and the dynamic behavior of the primary users. In this paper, we formulate the decision problem as a Markov decision process to obtain the optimal channel-action pair for the secondary user that maximizes his average throughput over the long run. In the proposed model, an incomplete information case is assumed where the secondary user does not know the actual state of the licensed channels. Moreover, sensing errors of missed detection and false alarm are considered and a general batch arrival stream is assumed. Numerical results show the superiority of the proposed optimal hybrid mode compared to the harvest-then-transmit mode in terms of both the average throughput and blocking probability, especially in the case of heavy primary user loads.