Performance Analysis of Wireless Energy Harvesting Cognitive Radio Networks Under Smart Jamming Attacks

In cognitive radio networks with wireless energy harvesting, secondary users are able to harvest energy from a wireless power source and then use the harvested energy to transmit data opportunistically on an idle channel allocated to primary users. Such networks have become more common due to pervasiveness of wireless charging, improving the performance of the secondary users. However, in such networks, the secondary users can be vulnerable to jamming attacks by malicious users who can also harvest wireless energy to launch the attacks. In this paper, we first formulate the throughput optimization problem for a secondary user under the attacks by jammers as a Markov decision process (MDP). We then introduce a new solution based on the deception tactic to deal with smart jamming attacks. Furthermore, we propose a learning algorithm for the secondary user to find an optimal transmission policy and extend to the case with multiple secondary users in the same environment. Through the simulations, we demonstrate that the proposed learning algorithms can effectively reduce adverse effects from smart jammers even when they use different attack strategies.