# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 2 Physical Layer Issues
### Interference Management  
A major issue arising not only in UAV networks but also in wireless networks in general is interference. Recently, ML-based approaches have been invoked for efficient interference management in UAV networks. The paper in Reference [43] studied opportunistic channel access by UAVs. This problem was formulated as a noncooperative interference mitigation game, considering the distinct characteristics of data traffic and UAV clustering. After, these characteristics are inserted in the utility function, appropriately allocating the weight coefficients to each characteristic and linearly combining them are performed. Moreover, a distributed log-linear learning algorithm is employed to achieve the Nash Equilibrium (NE) of the interference mitigation game. The learning algorithm is based on the fact that an UAV, suffering from intra-cluster interference, is randomly chosen to update its joint channel-slot selection according to its experienced interference level, slot interval and cluster rewards in each step and stochastically determines the channel selection. The simulations focused on convergence behavior, selection behavior, and performance evaluation, outlining the importance of setting appropriate weights for improved interference management by the log-linear algorithm. In this way, the proposed algorithm converges fast to achieve the optimal network utility and the minimum weighted interference level.  
Interference mitigation in networks where both ground nodes and UAVs are connected through a cellular is studied in Reference [44]. The goal of this study is twofold: first, to develop a path-planning scheme for the UAVs taking into consideration the interference that is introduced to the ground nodes and, second, to minimize the latency of the data related to their flight plan. So, an ESN-based DRL framework for online UAV trajectory optimization is presented and a noncooperative game is formulated consisting of the UAVs as the players and having the objective of learning their paths and of transmitting power level and cell association in an autonomous manner. Moreover, to overcome the hurdle of knowing the entire ground topology, the UAVs adopt the RL ESN-based algorithm to predict the ground network dynamics and pto lan their trajectories and resource allocation. Performance evaluation showed that the proposed approach is able to reduce the wireless latency for the UAVs and to increase the rate for the ground UEs at a complexity that is comparable to the conventional shortest path scheme.  
Then, the paper in Reference [45] proposed the use of LTE-Unlicensed (LTE-U) for deploying UAV BSs to establish wireless connectivity after a natural disaster. In such settings, coverage is limited due to damages of the ground LTE infrastructure while Wi-Fi connectivity might be possible in some areas. Thus, this study focused on interference management between LTE-U and Wi-Fi transmissions. As a result, deriving the optimal user association strategy between these networks is crucial and this problem was formulated as a game between the Wi-Fi access points (APs) and LTE-U UAV BSs. The UAV BSs aim to maximize their capacity without maximizing the interference of the Wi-Fi network. Thus, the LTE-U transmission period was configured by employing a regret-based learning dynamic duty cycle selection technique, maintaining a balance for the throughput of both networks. The performance evaluation results revealed that, in coexisting LTE-U UAV BSs and Wi-Fi APs topologies, the learning-based duty cycling improved the throughput by 32% when compared to fixed LTE-U duty cycling while an improvement of 10% was achieved over Q-learning-based duty cycling.