# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 4 Resource Management and Network Planning
It was shown that the RL approach achieves higher overall peak user coverage rates with the cost of slower convergence and the risk of occasional dips while the swarm intelligence-based solution resulted in lower coverage peaks and improved coverage stability and convergence. Next, the authors in Reference [71] proposed and applied network-based heterogeneous particle swarm optimization (NHPSO) in an air-to-ground downlink communication scenario, where multiple UAVs were deployed to provide wireless connectivity to a set of quasi-stationary heterogeneous users uniformly distributed in a geographical area with different required data transfer rates. The goal of NHPSO learning algorithm was to find the global optimum of a complex radio coverage problem while maximizing users' total required data rates. This was obtained by simulating the swarm in real-world complex systems following a heterogeneous learning strategy of particles along with the heterogeneous network topology structure. A typical manned and unmanned (MUM) airborne network consisting of both manned aircrafts (in higher layer) and UAVs (in lower layer) forming dynamic swarm network topologies was considered in Reference [72]. To find out the optimal link between two UAV relay nodes that maximizes the throughput performance, a DQN buffer-based model having the ability to store the network topology changes was proposed and the link selection was realized in the aircrafts (control nodes), which could accomplish heavy computational tasks in real-time. Also, an optimization algorithm was designed and implemented at the UAVs to handle the optimal placement of one or more UAVs as relays, under traffic QoS constraints and time-varying link conditions. The results pointed out that the DQN-based optimization algorithm surpassed the Q-learning algorithm. The uplink wireless channel of an UAV-aided NOMA, energy-efficient, and self-adaptive public safety network (PSN) with clusters of UEs was considered in Reference [73]. The role of UEs, i.e., either clusterheads having a critical role within the PSN or cluster members, was autonomously determined in a distributed manner based on the minority games (MG) theory [74] and Q-learning. To form the clusters, the cluster members acted as stochastic learning automata choosing their clusterhead based on RL and considering their physical proximity and the energy availability. After the cluster formation, the 3-D placement of the UAV was optimized in order to maximize the clusterhead's energy availability. The optimal transmission power (unique Nash equilibrium) of each UE is determined using a noncooperative game-theoretic method. In the extensive numerical results, the energy efficiency and the effectiveness of the proposed distributed decision-making method were validated, outlining its importance for the efficient operation of PSNs, where often centralized coordination might be unavailable. Recently, a novel paradigm has been proposed in wireless networks, i.e., content caching at network nodes towards minimizing the backhaul/fronthaul load and reducing the transmission latency. Due to their flexibility, UAVs have been considered to exhibit a pivotal role towards improving the performance of cache-enabled networks. The authors of Reference [75] exploited user-centric information related to content request distribution and mobility patterns for deploying UAVs and for determining content caching on their buffers. The optimization targets were to maximize the QoE and to minimize the total transmit power used by the UAVs. Assuming that cloud computing provides the necessary resources for the accurate prediction of content and user parameters, the optimal caching strategies are provided to the UAVs. The individual user behavior is classified by the cloud into distinct patterns through a concept-based ESN method, enabling improved accuracy through ML-based prediction. As a result, the ESN-based method facilitates the extraction of the user-UAV association, UAV placement, and content caching strategy.