## 1 Introduction
Specifically, users in the UAV group sense the available channel state and co-channel interference signal energy and make a judgment whether they are influenced by co-channel interference. For the case where users are not influenced by co-channel interference, an MDP is formulated to model the anti-jamming problem for the users, and an independent Q-learning method is employed to obtain the users' channel selection strategies. For the case where users are indeed influenced by co-channel interference, a Markov game is formulated, and a multi-agent Q-learning method is designed for UAV communication networks to obtain joint channel selections. Via using the learning experience, users can realize a quick anti-jamming channel selection decision in UAV communication networks. To sum up, the main contributions are summarized as follows:  
* A cooperative anti-jamming mechanism is designed for UAV communication networks, where UAVs cooperate via joint Q table sharing. Considering the influence of co-channel interference, an MDP and a Markov game are formulated, respectively. * An interference-aware cooperative anti-jamming distributed channel selection algorithm (ICADCSA) is designed for the anti-jamming selection problem. Without the influence of co-channel interference, an independent Q-learning method is adopted, while under the influence of co-channel interference, a multi-agent Q-learning method is employed. * Simulation results exhibit the performance of the proposed ICADCSA, which can avoid the malicious jamming and co-channel interference effectively. Moreover, the influence of channel switching cost and cooperation cost are investigated. Comparing this paper to our previous works [25; 31], which studied the anti-jamming channel selection in wireless communication networks, and to our previous work [32], we summarize the main differences as: (i) The work in [31] investigated the multi-agent learning method for anti-jamming problem, and [25] considered the single reinforcement learning in a fading environment. However, neither of them took the mobility of UAVs into consideration; whereas in our paper, the anti-jamming channel selection approach in UAV communication networks is investigated, taking the mobility of UAVs into consideration, which causes the variation of the co-channel interference level. Moreover, channel switching cost and cooperation cost are introduced, which influence the users' utilities. (ii) In [32], we focused on the anti-jamming power control problem in UAV communication networks, whereas in this paper, the cooperative anti-jamming channel selection scheme is designed, and a cooperative anti-jamming algorithm based on multi-agent Q-learning is derived, which obtains strategies by interacting with the environment. The rest of this paper is as follows. In Section 2, the system model and problem formulation are investigated. In Section 3, the interference-aware cooperative anti-jamming mechanism in the UAV group is designed. In Section 4, the proposed interference-aware cooperative anti-jamming distributed channel selection algorithm (ICADCAS) is shown, and the complexity is analyzed. Moreover, a discussion of the quick decision for UAVs is also presented. In Section 5, simulations and discussions are conducted. In the end, we make a conclusion and investigate future work in Section 6.