# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 4 Resource Management and Network Planning
The simulation results have proved that the coexistence of the RL approach with the k-means algorithm can effectively reduce the handoffs by 75% and can simultaneously increase the system throughput compared to UAV mobility control based on SNR estimation. Furthermore, in Reference [65], a cellular network is considered that consists of a set of UAVs and a set of ground BSs, which both may serve the users. The main objective is to predict traffic congestion in order to deploy temporary UAV BSs with minimum requirements regarding the communication and mobility powers. This offloading strategy is achieved by introducing a Gaussian mixture model that is based on a weighted expectation maximization algorithm. Performance evaluation has shown that the proposed ML framework can reduce the requirements for the downlink communication power by 20% and for the mobility power by 80% as compared to UAV deployments without the benefits of ML. In Reference [66], an UAV-enabled LTE-U network is assumed in which the UAVs are able to access both the licensed and unlicensed bands. The goal of this work is to develop an efficient resource allocation scheme that will maximize the QoS and simultaneously satisfy the delay requirements. To this aim, an ESN algorithm has been proposed with leaky integrator neurons, which enables the UAVs to adopt the environmental dynamics. The simulation results showed that the proposed approach achieves up to 14% and 27.1% QoS gains for the UAV associated users, compared to Q-learning using LTE-U and Q-learning using LTE. Such gains are attributed to the dynamic adjustment of the ESN's state update speed, thus enabling the UAVs to learn the dynamics of their associated users. In Reference [67], in order to enable efficient multi-modal, multi-task offloading, a new architecture is proposed for UAV clustering. For facilitating UAV cooperation and joint optimization of the computation, caching, and communication resources, an AI-based decision making framework has been proposed in terms of the RNN. To this aim, UAV cluster deployments have been employed, which are based on both historical data mining and real time perception. The numerical results presented have shown that the proposed strategy can effectively improve the collaboration between the UAC clusters. Moreover, in Reference [68], a cluster-based network has been assumed, where multiple UAVs perform various real-time time tasks, e.g., sensing and transmitting. It is noted that the communication part is based on orthogonal frequency division multiplexing (OFDM). In this framework, RL has been used to solve the allocation problem of wireless channels in dynamic conditions, targeting the optimization of the time delay of UAV data transmissions. The numerical results presented showed the applicability of the proposed framework for real-time UAV scheduling. In addition, RL does not rely on accurate models, thus making it a well-suited solution for UAVs with limited processing capabilities while relaxing the necessity for large offline data sets, as it is the case in supervised learning approaches. In Reference [69], a scenario where a licensed primary user (PU) shares its spectrum with a network of UAVs is considered. In this scenario, the UAVs can be used as relays for the primary network or can perform data transmission to the fusion center. The objective is to maximize the total utility of the PU and UAV network. To satisfy this objective, a deep distributed reinforcement learning (DDRL) algorithm is employed by which the UAVs chose their role without requiring exchange of information or the knowledge of other's UAVs decisions. The presented numerical results verify the convergence of the proposed algorithm as well as the maximization of the network resources. Various works leveraged swarm-based optimization tools in UAV networks. In Reference [70], a network with multiple high amplitude platform stations (HAPS) is considered for communication coverage purposes. The coordination of this network has been performed with the aid of RL and swarm intelligence.