Paper Info
Title
Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter.
Abstract
For an RF-powered cognitive radio network with ambient backscattering capability, while the primary channel is busy, the RF-powered secondary user (RSU) can either backscatter the primary signal to transmit its own data or harvest energy from the primary signal (and store in its battery). The harvested energy then can be used to transmit data when the primary channel becomes idle. To maximize the throughput for the secondary system, it is critical for the RSU to decide when to backscatter and when to harvest energy. This optimal decision has to account for the dynamics of the primary channel, energy storage capability, and data to be sent. To tackle that problem, we propose a Markov decision process (MDP)-based framework to optimize RSU's decisions based on its current states, e.g., energy, data as well as the primary channel state. As the state information may not be readily available at the RSU, we then design a low-complexity online reinforcement learning algorithm that guides the RSU to find the optimal solution without requiring prior- and complete-information from the environment. The extensive simulation results then clearly show that the proposed solution achieves higher throughputs, i.e., up to 50%, than that of conventional methods.
Year
DOI
Venue
2018
10.1109/GLOCOM.2018.8647862
IEEE Global Communications Conference
Keywords
DocType
Volume
Ambient backscatter,RF energy harvesting,cognitive radios,MDP,reinforcement learning
Conference
abs/1808.07601
ISSN
Citations 
PageRank 
2334-0983
2
0.37
References 
Authors
6
6
Name
Order
Citations
PageRank
van huynh nguyen19610.63
Dinh Thai Hoang2141377.92
Diep N. Nguyen314226.31
Eryk Dutkiewicz4891122.78
Niyato Dusit59486547.06
Ping Wang64153216.93