1. Introduction
Since 2019, telecommunication companies have started to deploy fifth-generation (5G) mobile communication networks to replace the existing fourth-generation (4G) communication networks. By 2025, the number of pieces of user equipment (UE) worldwide is expected to grow to 1.7 billion [
1]. Ericsson Telecommunications predicts that the global mobile data traffic volume will reach 370 EB monthly by 2027, which is 4.4 times the monthly volume of 80 EB at the end of 2021. 5G systems are expected to carry 62% of mobile data traffic by 2027 [
2]. The 5G network architecture is cellular, and the service area is divided into numerous small cells. All the UE in each cell transmit radio waves to communicate with the base station (BS) that covers the cell. The BSs are wired through high-bandwidth fiber optics to the public switched telephone network and to routers for Internet access. The millimeter-wave (mmWave) frequency band and multiple in, multiple out (MIMO) technology are used to increase network capacity, and therefore, 5G may be able to support up to one million mobile devices per square kilometer, which is 10 times the number of mobile devices supported by 4G networks over the same area. Identical to the existing cellular networks, handover occurs when a device moves from one cell to another cell. On the other hand, joint beamforming design and optimization, security, and energy efficiency of mmWave and MIMO are continuously investigated for satellite communications [
3,
4,
5].
With the aforementioned explosive growth in traffic volume, cellular network fragmentation and densification have emerged as the most effective technologies for increasing network capacity and improving user experience. In recent years, ultra-dense networks (UDNs) have been developed as a crucial solution to support capacity densities of up to 10 Mbps/m
2 in 5G networks. However, the propagation distance of millimeter radio waves is up to 300 m. Therefore, smaller BSs must inevitably be set up at intervals of a few hundred meters to extend the service coverage, resulting in a UDN-like architecture. In terms of cell coverage, BSs are classified as macro-, micro-, femto-, and picocells depending on their cell radii. Among them, micro-, femto-, and picocells are considered small cells. Several definitions of UDNs have been proposed in terms of cell quantity. In [
6,
7], a UDN is defined as a cellular network with a BS density that may be equal to or greater than the user density, which is suitable for describing a scenario in which user traffic increases but the number of users does not. Alternatively, a UDN is defined as a cellular network that offers coverage of a few meters between any two BSs [
8]. In addition, a UDN is a cellular network in which the number of BSs increases linearly as the demand for network capacity increases [
9].
As depicted in
Figure 1, a 5G UDN is typically a heterogeneous network composed of dense mmWave BSs and traditional 4G BSs. Although 5G UDNs can significantly improve network capacity, they are fraught with multiple problems. For instance, inter-cell interference is more severe, optimal utilization of radio resources is more complex, and load distribution among BSs is more unbalanced. Specifically, the UE moving within a UDN are likely to encounter more frequent handover opportunities that lead to handover-related problems, such as premature/late handover decisions and the ping-pong effect. Handover execution at an inappropriate time may interrupt data transmission links. The ping-pong effect refers to the back-and-forth handover phenomenon between two BSs that is caused by unstable signal strength resulting from the movement of UE close to the edge of a BS’s coverage. As summarized in
Table 1, the 3rd Generation Partnership Project (3GPP) defines the trigger scenarios of handover decision for cellular networks [
10]. A-series events correspond to horizontal handovers, that is, handover between two BSs based on the same radio access technology (RAT), whereas B-series events correspond to vertical handovers, that is, handover between two BSs based on different RATs.
Starting with 4G networks, Event A3 has been implemented for handover triggering. 5G networks also use Event A3 for performing handovers. As depicted in
Figure 2, when the signal strength of the neighbor BS is greater than that of the serving BS plus a threshold value and an offset, which is designated as the handover margin (HOM), handover is performed after waiting for a time-to-trigger (TTT) duration. HOM and TTT are used to make handover decisions to avoid the ping-pong effect and alleviate the control burden of BSs. The 5G era is characterized by larger bandwidth, lower latency, and smaller cell coverage. In this context, mmWave technology can support high-density BS deployment for hotspots, perform high-precision positioning of UE, and facilitate equipment integration. Therefore, it is beneficial to promote the miniaturization of base stations and user terminals.
The UE moving within a 5G UDN may encounter more frequent handover opportunities because the cell coverage of each small BS is not as wide as that of 4G BSs, and handover scenarios in 5G UDNs appear to be more complex. In 4G network environments, handovers are only performed at the cell edge of a BS, and there are fewer target BS candidates. Consequently, the use of Event A3 for making handover decisions can lead to good performance. However, the use of Event A3 with fixed HOM and TTT for making handover decisions is not reliable for 5G UDNs, because UE may frequently move forward into a certain area where the cell coverage of multiple BSs overlaps. Moreover, individual handover parameter values are essential for different UEs to adapt to diverse handover scenarios. For example, when the signal strength of the serving BS is good, a high HOM can prevent unnecessary handovers or the ping-pong effect. In addition, for UE moving at a high speed, small HOM and TTT can lead to more successful handovers in time.
Fuzzy logic (FL) [
11,
12] is a well-known method for translating the domain knowledge of human experts into a rule base and for experts to formalize uncertainty. FL is considered the best technique to unravel the handover problem in a high-density scenario of small cells for 4G/5G networks [
13]. Recently, numerous FL-based handover schemes, which are elaborated in
Section 2, have been proposed for making handover decisions more accurately in time. To alleviate unnecessary handovers and avoid a severe ping-pong effect in 5G UDNs, we propose an FL-based handover algorithm with dynamic HOM and dynamic TTT (abbreviated as FLDHDT) by using the signal to interference plus noise ratio (SINR) and horizontal moving speed of UE as inputs to the FL controller. We performed simulations using the well-known ns-3 simulator to demonstrate the effectiveness and superiority of the proposed FLDHDT. The performance measures include the number of handovers, overall system throughput, and ping-pong ratio.
The remainder of this paper is organized as follows.
Section 2 explores the related literature.
Section 3 describes the operating procedures of the proposed FLDHDT.
Section 4 analyzes and compares the simulation results. Finally, our concluding remarks and an outline of future works are summarized in
Section 5.
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]. Yajnanarayana 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. Zhao 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. Banna et al. [
24] presented a fast adaptive handover algorithm by using FL 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. Saddam Alraih et al. [
25] proposed a robust handover optimization technique with FL controller (RHOT-FLC). The proposed RHOT-FLC aimed to automatically configure both HOM and TTT by exploiting the information on RSRP, reference signal received quality, and UE velocity as input factors. Additionally, the inference rules were determined based on the evaluation conducted on each rule and all of the performance metrics to achieve mobility robustness optimization at the cost of computation loads and times.
Because FL can provide relatively reliable outcomes with lower computational costs and shorter times, it has been widely applied in many fields, including for making handover decisions in mobile cellular networks. The correctness of the inference engine of FL depends on the choice of input factors, membership functions (MFs), rule base size, and others. The main contribution of our work is the proposed adaptive handover decision algorithm that uses FL to effectively alleviate unnecessary handovers and reduce the ping-pong effect in 5G UDNs by dynamically adjusting TTT in addition to HOM. The inputs to the FL controller are only the signal strength and horizontal moving speed of user devices, and the outputs are the HOM and TTT values for each piece of UE.
5. Conclusions
In this study, we have proposed an FL-based handover scheme for 5G UDNs. The proposed scheme can dynamically adjust two handover parameters, namely HOM and TTT, by using the SINR and horizontal moving speed of UE as inputs to an FL controller. The proposed scheme is designated FLDHDT. Similar to other handover decision schemes, signal strength is considered an important factor when making handover decision in FLDHDT. A higher signal strength inevitably leads to a higher HOM. Moreover, UE velocity is an input factor governing handover decision in FLDHDT. A higher UE speed warrants a shorter TTT. The proposed FLDHDT reduces the number of unnecessary handovers and shortens the interruption gap caused by hard handovers. Moreover, only the BSs in front of the UE can be the target BS candidates to ensure more accurate selection of target BS within a relatively short time. The superiority of FLDHDT has been verified by conducting a simulation in which it substantially improved the handover performance of 5G UDNs in terms of the number of handovers, ping-pong ratio, and overall system throughput compared to a conventional handover scheme, namely Event A3, and an FL-based handover scheme with dynamic adjustment of only HOM. In the future, we intend to design a more practical FL-based handover scheme by considering different coverages of small BSs and providing additional inputs to the FL controller, such as BS loading.