are discussed in this section. LTE "OFF" LTE "ON" LTE "OFF" LTE "ON" Figure 10.6 Duty cycle-based method. 10 Secure Spectrum Sharing with Machine Learning: An Overview Controller A Controller B Wi-Fi AP (101) Cluster UE(Wi-Fi) UE(LTE-U) (191) (191) Wi-Fi AP Small Cell UE(Wi-Fi) Wi-Fi AP Figure 10.7 The coexistence of LTE-U and WiFi in an unlicensed spectrum. Source: [26] (© [2016] IEEE). An SS scheme adapted for the operation of LTE-U and WiFi systems was proposed in [26]. The decision tree learning and repeated game were adopted to optimize unlicensed spec- trum resource. As shown in Figure 10.7, the control plane was decoupled from the data plane and provided the system with a great capacity for processing data. Controllers learned the latest dataset in the pool to build decision trees and deduce the network status of the opponent. Repeated games for sharing spectrum resources were then employed to maxi- mize resource utilization in the coexistence system. An incentive mechanism was further included to increase operators' motivation to share their spectrum resources. 10.2.3.4 Distributed-Algorithm-Based Methods In [31], the authors investigated the uplink-downlink decoupling resource allocation prob- lem for LTE-U-enabled Small Cell Networks (SCNs). By incorporating user association, spectrum allocation, and load balancing, the problem was formulated as a non-cooperative game. An Echo State Network (ESN)-based distributed algorithm was developed to address this problem. It was shown that even with limited information on the network's and users' states, the proposed algorithm could help the SBS to choose their optimal resource allocation strategies autonomously. Furthermore, the proposed method could significantly improve the sum rate of the 50th percentile of users and achieve a 167% increase compared to a Q-learning algorithm. In [32, 33], a cache-enabled Unmanned Aerial Vehicles (UAVs) communication network serving wireless ground users over the LTE-U bands was considered. The problem under investigation was 