Performance Optimization for Cooperative Multiuser Cognitive Radio Networks with RF Energy Harvesting Capability

We study the performance optimization problem for a cognitive radio network with radio frequency (RF) energy harvesting capability for secondary users. In such networks, the secondary users are able to not only transmit packets on a channel licensed to a primary user when the channel is idle, but also harvest RF energy from the primary users' transmissions when the channel is busy. Specifically, we propose a system model where the secondary users are able to cooperate to maximize the overall network throughput through sensing a set of common channels. We first consider the case where the secondary users cooperate in a TDMA fashion and propose a novel solution based on a learning algorithm to find optimal channel access policies for the secondary users. Then, we examine the case where the secondary users cooperate in a decentralized manner and we formulate the cooperative decentralized optimization problem as a decentralized partially observable Markov decision process (DEC-POMDP). To solve the cooperative decentralized stochastic optimization problem, we apply a decentralized learning algorithm based on the policy gradient and the Lagrange multiplier method to obtain optimal channel access policies. Extensive performance evaluation is conducted and it shows the efficiency and the convergence of the learning algorithms.