In the last decade, the behavior of mobile data users has completely changed. We are no longer limited to our own house if we want to share pictures, make a video call, stream music or video, or game. In fact, nowadays, we can do all those things in the park, bus, or train, actually wherever we are. With the upcoming next generation standard--the 5 th Generation or 5G--not only will significantly higher data rates will be offered, but also an ever larger spectrum of applications and services to the mobile users and platforms, all with heterogeneous requirements. Besides serving users, 5G will also bring many opportunities to the industry. This means that 5G will be serving a massive density of nodes which can either be human-held or machine-type; a tendency we currently already see in the emerging Internet of Things (IoT) and its applications. The above-mentioned evolution will of course also have its repercussions on the network itself. The future wireless network will be characterized by a high degree of flexibility compared with the past, allowing them to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demand evolutions both in time and space. The networks of today are designed to cope with average or peak traffic predictions and the offered capacity on a local scale is highly dependent on the density of the infrastructure equipment in a considered area. Adding mobile infrastructure or UABSs (Unmanned Aerial Base Stations)--i.e., a base station mounted on a UAV (Unmanned Aerial Vehicle) or drone--would provide a huge added value to the network. One of the key challenge of designing such a UAV-based network is a proper allocation of both the UAV's location and network resources. Ref. [1] proposed a fast and practical algorithm for UAV relay networks to provide the optimal solution for the number of transmit time slots and the UAV relay location in a sequential manner. The transmit power at the base station and the UAV was determined in advanced based on the availability of channel state information (CSI). Simulation results demonstrated that the proposed algorithms can significantly reduce the computational effort and complexity to determine the optimal UAV location. Ref. [2] formulated the uplink resource allocation problem for a NOMA (Non-Orthogonal Multiple Access)-IoT based UAV network with the objective of maximizing the system throughput while minimizing the delay of IoT applications. Power allocation was investigated to achieve fairness between the users. The algorithm achieved a 31.8% delay improvement, 99.7% reliability increase, and 50.8% fairness enhancement when compared with the maximum channel quality indicator CQI) algorithm, allowing the algorithm to be used for URLLC (Ultra-Reliable Low-Latency Communication) applications. Due to the large-scale deployment of UAVs in UAV-based networks, also the shortage of spectrum is a realistic threat for the roll-out of these networks. Ref. [3] proposed a cooperative spectrum sensing scheme for cognitive UAV networks based on a CHMM (Continuous Hidden Markov Model) with a novel SNR (Signal-to-Noise Ratio) estimation method. Another typical use case for UAV-aided networks--besides offering an extra degree of flexibility in the network--is to extend the network's coverage in isolated or rural areas. Ref. [4] comprehensively surveyed the user of three platforms to deliver broadband services to remote and low-income areas: UAVs, APs (Altitude Platforms), and LEO (LowEarth Orbit) satellites. The UAVs are considered as a noteworthy solution since their efficient maneuverability can solve rural coverage issues or not-spots. Ref. [5] investigated six different routing algorithms for this kind of applications. A time-dependent variant of the [PERSON]'s algorithm which determines the fastest route by taking into account the time when the message reaches the node and the time allocated for data transfer was developed for a DTN (Delay Tolerant Network) of drones flying in an aerial area divided either into squares or into equilateral triangles.