An Adjustable Channel Hopping Algorithm for MultiRadio Cognitive Radio Networks Abstract   In cognitive radio networks, achieving rendezvous for two nodes (switching to the same channel at the same time) is a premise for them to communicate. Applying a channel hopping protocol is a common solution to achieve rendezvous. Most existing channel hopping protocols work in a singleradio environment and use available channels evenly without considering the timevarying channel conditions for different channels. Such channel hopping protocols may suffer from poor performance due to inefficient channel allocation. To efficiently solve the rendezvous problem, we propose the Adjustable Multiradio Channel Hopping (AMCH) protocol in this paper. Instead of using available channels evenly, nodes running AMCH are able to adjust the usage ratio of each available channel. Simulation results verify that such a ratio adjustable channel hopping mechanism provides an efficient way to solve the rendezvous problem.

A FrequencyHopping Game Between a Smart Jammer and a Cognitive Radio A smart jammer can determine the frequencyhopping patterns of a cognitive radio using machine learning. This paper develops a twostate stochastic game where the receiver and jammer choose at each stage whether to stay on their current frequency or hop to a new one. The cost of hopping to a new frequency is also modeled with a discounted payoff. Machine learning algorithms are implemented to update the policies for both players in order to converge to a stationary Nash equilibrium. The game is simulated using Nash Qlearning and WoLF PHC, as well as the computed singlestage Nash equilibrium for each state. Of the strategy options included for both players, there are ideal strategyupdating algorithm pairs depending on the values used for the discounted payoffs.