# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 4 Resource Management and Network Planning
The simulations reveal that the UAV transmit power can be adequately reduced compared to an algorithm without caching as well as conventional ESN approaches, while QoE is significantly increased compared to a topology without UAVs. Then, focusing on a LTE cloud network operating in licensed and unlicensed bands, resource allocation for cache-enabled UAVs supporting ground users was studied in Reference [76]. Constrained by the limited capacity of the UAV-cloud links, LSM is employed to help the UAVs perform content caching and resource management. LSM enables the cloud to efficiently learn user-centric information regarding content request distribution and to facilitate spectrum allocation by the UAVs. This method is extended in Reference [77], where an optimization problem is formulated to maximize the number of users with stable queues. As a solution to this problem, a self-organizing, decentralized algorithm is developed and LSM is employed for joint caching and resource allocation over both licensed and unlicensed bands. Then, performance evaluation is conducted illustrating the increase in the number of users with stable queues in comparison to Q-learning with and without content caching. In a similar setting, with cache-enabled UAVs, the paper in Reference [78] introduced proactive caching for latency and backhaul load reduction. Thus, a multi-objective optimization problem is formulated targeting parameters, such as minimum number of deployed UAVs, transmit power, UAV-user association, and cache location. To solve the optimization problem, RL is adopted for user grouping, performing local search according to the optimal UAV deployment over each group. From the results, the efficiency of the RL method is shown, effectively minimizing the number of required UAVs and their 3-D placement in the network. Table 4 summarizes all the resource allocation and network planning techniques which employ ML algorithms for UAV-enabled communication networks. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Resource Allocation Target** & **ML Solution** \\ \hline [PERSON] et al. [61] & UAV connectivity prediction & LR and SVM \\ [PERSON] et al. [62] & Predict best serving cell & Conditional random field \\ [PERSON] et al. [63] & Connectivity recovery in disastrous area & k-means \\ [PERSON] et al. [64] & Reducing handoffs & RL \\ [PERSON] et al. [65] & Deploy UAVs BSs & Gaussian mixture model \\ [PERSON] et al. [66] & Resource allocation & Echo state networks \\ [PERSON] et al. [67] & Multi-modal multi-task offloading & Recurrent neural network \\ [PERSON] et al. [68] & Dynamic task scheduling & RL \\ [PERSON] et al. [69] & Optimizing the PU and UAV network resources & Distributed RL \\ [PERSON] et al. [70] & HAPS network coordination & RL \\ [PERSON] et al. [75] & UAV deployment and caching & ESN-based \\ [PERSON] et al. [76; 77] & UAV spectrum allocation and caching & LSM-based \\ [PERSON] et al. [78] & UAV spectrum allocation and caching & RL \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Resource allocation and network planning issues in ML-enhanced UAV networks.