Their simulation results showed a better performance of the proposed time-dependent Dijkstra algorithm compared with state-of-the art routing protocols. One step further than extending the coverage of an existing network is to provide wireless communication in areas hit by a natural disaster. Ref. [6] jointly addressed the transmission power selection, data-rate maximization, and interference mitigation problems for such a scenario. Considering all these conflicting parameters, the problem was investigated as a budget-constrained multi-player multi-armed bandit problem. The whole process was carried out in a decentralized manner, where no information was exchanged between UAVs. To achieve this, two power-budget-aware algorithms were proposed to realize the selection of the transmission power value efficiently. Both algorithms showed outstanding performance over random power value selection in terms of achievable data rate. UAVs have also showed their added value for the enhancement of wireless communication in millimeter-wave bands. Typically, antenna arrays have been employed for this purpose. However, many beam-forming methods for improving communication quality are based on channel estimation, which are resource-intensive due to the complexity of channel estimation in practice. Ref. [7] formulated a MIMO (Massive Input Massive Output) blind beamforming problem at the receivers for UAV-assisted communications in which channel estimation was omitted to save resources. An analytical method, called ACMA (Analytical Constant Modulus Algorithm), was introduced relying only on data received by the antenna. ACMA could achieve good signal recovery accuracy, a reasonable sum rate, and acceptable complexity. Many challenges related to the design of UAV networks in general, such as, for example, spectrum sharing/coexistence, are computationally very challenging and hence an excellent case to apply machine learning and AI (Artificial Intelligence) on. Ref. [8] proposed a multi-agent DQL (Deep Q-learning)-based transmission power control algorithm to minimize the outage probability while satisfying the interference requirement of an interfered system. To deal with the potential risk of action-value overestimation from the DQL, they developed even a DDQL (Double DQL). The proposed DQL power control algorithm performed equal or close to the optimal exhaustive search algorithm for varying positions of the interfered system. With a similar performance by the DDQL power control yields the same performance, the authors concluded that the actual value overestimation did not adversely affect the quality of the learned power control policy. Besides using machine learning to actual develop the network, it can also be applied in properly predicting the user demand. Ref. [9] proposed a UAV positioning algorithm with the objective of extending an existing network and balancing the traffic load. To properly predict the users and their movement, the performance of various state-of-the-art machine learning algorithms was investigated. Random Forest and Gradient Boosting presented both the best performance, with Random Forest having a better prediction and training time. The use of UAVs in wireless communication networks--whether it is to provide extra flexibility, extend the coverage or cover isolated, rural, or post-disasters areas--makes the network more vulnerable for security issues. Ref. [10] proposed a security model applying cooperative friendly jamming using artificial noise and drone mobility to prevent eavesdropping and improve security. Ref. [11] developed two dynamic selection techniques which identify the most effective classifier for the detection of GPS (Global Positioning System) spoofing attacks. It might be clear that using UAVs for wireless communication and networks can provide a tremendous added value. The scenarios here presented are only a fraction of what we can achieve by UAV-aided communication. A nice overview of what is possible was provided in ref.