# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 2 Related Works  
There are two types of handover, namely hard and soft. The transmission channel inevitably has a short gap in hard handover because the channel connected to the serving BS is released before it is connected to the target BS. The advantage of hard handover is that only one channel is used for a UE at any time, and its disadvantage is that the transmission channel may be temporarily interrupted or even abnormally terminated if the handover fails. Soft handover retains the channel connected to the serving BS and uses the channel connected to the target BS in parallel for a certain period. The advantage of soft handover is that the connection link to the serving BS is disconnected after a reliable connection link to the target BS is established, whereas its disadvantage is that the system capacity decreases because multiple channels are occupied during a handover procedure. Because the cost of hard handover is considerably lower than that of soft handover and the interruption gap of hard handover is acceptable for maintaining the desired transmission quality, all mobile wireless networks implemented have used hard handover. Accordingly, most of the recent works on handover decisions have investigated hard handover. As mentioned in Section 1, Event A3 is used as the trigger scenario to make handover decisions in 4G/5G networks. Two handover parameters, HOM and TTT, are used to decide when to execute handover from the serving cell to the target cell. Once the reference signal receiving power (RSRP) of the target BS is higher than that of the serving BS plus HOM for the duration of TTT, handover execution is performed. One of the drawbacks of using Event A3 is that HOM and TTT are fixed for each UE. Moreover, only the reported RSRP is used as the input in the handover decision procedure without considering other factors, such as UE speed, distance between BS and UE, and BS loading. The authors of [14; 15] described software-defined networking (SDN)-based handover decisions for 5G UDNs. Their SDNs are responsible for a global view of network topology and centralized control to facilitate seamless handovers in cellular/IEEE 802.11p hybrid networks and to shorten the handover execution time, respectively. On the other hand, numerous authors applied artificial intelligence techniques to handover management in 5G [16; 17]. [PERSON] et al. [18] proposed a novel method for maximizing the long-term link-beam RSRP after handover by using reinforcement learning for three pre-defined deployments of 5G cellular architecture. [PERSON] et al. [19] presented a model for predicting user trajectory by combining convolutional neural networks and long short-term memory to identify the traffic demands of all BSs in advance to improve network resource utilization and user experience in 5G UDNs. Recently, many researchers [20; 21; 22; 23; 24; 25] have used FL to design adaptive handover decision schemes for 5G. The authors of [20; 21] proposed FL-based handover algorithms to dynamically adjust only HOM. The difference between the two algorithms proposed in these studies is that the former uses only SINR as an input to the FL controller, while the latter uses UE speed and SINR. The authors of [22; 23] developed vertical handover decision algorithms considering various RATs by taking into account SINR, bandwidth, cost, or UE velocity. The output of their FL controller is the most suitable RAT for accessing the Internet, aiming to reduce the number of interruptions and handoff processing delays. [PERSON] et al. [24] presented a fast adaptive handover algorithm by usingFL in 5G cellular networks only for high-speed trains, which operate at speeds equal to or higher than 400 km/h. Their algorithm comprises two phases. The first is selection of the target BS in advance according to the trajectory of the train. The other is to dynamically adjust the TTT in the range of [0, 250 ms] by using cell size and train speed as inputs to the FL controller. [PERSON] et al.