## 2 System Model and Problem Formulation  
The system model is shown in Figure 1. We construct the UAV communication network, which is a kind of dynamic UAV canonical network. Moreover, we assume that there are \(N\) users and one malicious jammer in the system scenario. A user in the UAV canonical networks is a collection of multiple UAVs with intra-communications, and there is a heading UAV managing the whole cluster, transmitting command and control information or sharing some important messages. For simplicity, we assume that UAVs in the same cluster keep relatively static and denote one UAV cluster as one user. Examples of users in traditional canonical networks are given in [18; 33]. Under these assumptions, we think when different users are close to each other, the existence of co-channel interference is a matter that influences the intra-communications of users. Moreover, in our system scenario, UAVs are under the threat of a malicious jammer and co-channel interference simultaneously. The locations of users in the UAV group are time-varying, and users can cooperate with each other via information exchange. The users' set is denoted as \(\mathcal{N}\)\(=\)\(\{1,...,n,...,N\}\), and the available channel set for the user is \(\mathcal{M}\)\(=\)\(\{1,...,m,...,M\}\). We consider two different cases of users' transmissions: (i) When users are close to each other, and transmitting in the same channel, high received signal energy from other users causes them to be influenced by co-channel interference. (ii) When users are far away from each other, the received signal energy from other users is somehow low; thus, users are not influenced by co-channel interference. The mutual interference threshold \(\tau_{0}\) is used to measure the influence of co-channel interference, that is: when the received co-channel interference signal energy is lower than \(\tau_{0}\), the UAV communication network is not influenced by co-channel interference, and vice versa. The co-channel interference threshold \(\tau_{0}\) is defined as the received signal energy threshold, which is transmitted from other users:  
\[\tau_{0}=p_{0}d_{th}^{-\alpha}, \tag{1}\]  
where \(p_{0}\) is the transmission power of users, \(d_{th}\) is the co-channel interference distance threshold among users and \(\alpha\) is the path-loss factor. We assume that channel strategy \(a_{n}\left(t\right)\) means user \(n\) chooses channel \(c_{n},c_{n}\in\mathcal{M}\), to transmit in time slot \(t\), \(a_{-n}\left(t\right)\) is the channel strategy combination of all users except user \(n\) and \(a_{j}\left(t\right)\) is the jamming channel. Note that in [31], the authors considered that if there existed co-channel interference in one channel, users were not able to transmit in this channel; while in our paper, we assume that if there exists co-channel interference, users can still transmit to their receivers via accessing the channel with a relatively fair pattern, which is more realistic. Then, the throughput of user \(n\) in slot \(t\) is expressed as:  
\[Tr_{n}\left(a_{n}\left(t\right)\right)\!,a_{-n}\left(t\right)\!,a_{j}\left(t \right)\right)=\left(1\!-\!f\left(a_{n}\left(t\right)\!,a_{j}\left(t\right) \right)\right)\frac{1}{I_{n}\left(c_{n}\right)}\text{log}_{2}\left(1+\frac{P_ {n}d_{n}^{-\alpha}}{N_{c_{n}}}\right), \tag{2}\]where \(d_{n}\) denotes the distance between the transmitter and the receiver of user \(n\), \(P_{n}\) represents the user \(n\)'s transmission power and \(N_{c_{n}}\) represents the channel noise power. Moreover, \(I_{n}\left(c_{n},t\right)\) is the congestion degree of channel \(c_{n}\) in the current slot, which is expressed as:  
\[I_{n}\left(c_{n},t\right)=\left\{ \begin{array}{l}1+\sum\limits_{x\in\mathcal{ N}/n}f\left(a_{n}\left(t\right),a_{x}\left(t\right)\right),\quad P_{x}d_{x,n}^{- \alpha}\left(t\right)\geq\tau_{0},\\ \ldots\\ 1,\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad P_{x}d_{x,n}^{- \alpha}\left(t\right)<\tau_{0}.\end{array} \right.