The Handover and Performance Analysis of LTE Network with Traditional and SDN Approaches

Abstract

The mobility of user equipment (UE) in cellular network is a challenging issue in terms of its management. Current traditional handover in Long-Term Evolution (LTE) network is managed by evolved Node B or eNodeB (eNB) which is a decentralized solution. In contrast to existing technology, software defined network (SDN) has the capability of serving the packets of the switching equipment without involving the SDN controller except for the first one. We have proposed an SDN-based centralized solution for handover management in LTE network. Based on our solution, the handover is being managed by SDN controller which keeps track of the overall network management and dictates the flow entries to the OpenFlow switches in the network. In our testbed, two UEs are connected to two eNBs, and one of the UE performs a handover from one eNB to other eNB. It enhances the performance of the network in terms of reducing the delay while performing the handover and increasing the data rate of the running application. The initial delay will be bit higher due to the initial flow entry absence in the flow tables of the switches; later, the delay will be reduced. It is evident from the results that our approach keeps track of the overall network at centralized controller with improved performance.

This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

1. Introduction

With the rapid growth in telecommunication industry, the generations of mobile network have evolved into multiple generations 1G, 2G, 3G, 4G, and 5G where G stands for generation and numbers 1, 2, 3, 4, and 5 represent the generation number. The performance analysis of SDN-based handovers in LTE network enhances the users’ potential problems such as decision-making of autonomous mobile vehicles (drones, driverless vehicles, etc.). The LTE network merging with SDN technology is the evolution of the Traditional LTE network.

The fast growth in Information and Communication Technology (ICT) leads the use of ICT applications from fixed computing devices to wireless and mobile communication devices. The ease in use of ICT applications is supported by high-speed telecommunication networks like 4G/LTE, LTE Advanced, LTE Advanced Pro, and 5G. According to Huawei, in 2040, most of the presentation by humans will be conducted by hologram technology using 6G networks. This evolution in the field of telecommunication is the result of rapid advances in research and development in the field of wireless and mobile communication.

3rd Generation Partnership Project (3GPP) is International Standards Organizational body which deals with the standardization of the protocols and standards in field of telecommunications and was founded in 1998. This organization is responsible for the development and maintenance of the following standards in the telecommunication industry:

(i) Global System for Mobile Communication (GSM) which covers 2G and 2.5G standards, including General Packet Radio Service (GPRS) and Enhanced Data rates for GSM Evolution (EDGE)

(ii) Universal Mobile Telecommunication Systems (UMTS) and related 3G standards, including High Speed Packet Access (HSPA) and High Speed Packet Access Plus (HSPA+)

(iii) Long-Term Evolution (LTE) and its enhanced versions including 4G standards, LTE Advanced, and LTE Advanced Pro

(iv) 5G New Radio (NR) is a new radio access technology (RAT) and its related 5G standards

The mobile communication has multiple parts in the mobile network: the mobile user equipment (UE), radio access network (RAN), and the core part of the mobile network.

In mobile network, different user equipments are used. This user equipment has dual wireless interfaces to be connected to cellular as well as with Wi-Fi networks based upon the availability of these networks. Mobile phones, tables, laptops, and IoT devices come into the category of wireless and mobile user equipment.

There are different types of base station nodes available in the RAN part of the mobile networks. As mobile networks have evolved from 2G (GSM) to 3G (UMTS), 4G (LTE), and 5G (NR) over time, so in the mobile RAN, different types of base stations are used in these generations of cellular network to provide the wireless connectivity to wireless devices of the mobile users.

In 2G mobile network, it was called base transceiver station (BTS). In 3G mobile network or Universal Mobile Telecommunication System (UMTS) or Evolved UMTS Terrestrial Radio Access Network (eUTRAN), it is called NodeB, which connects with Radio Network Controller (RNS). In 4G (LTE) mobile network, it is called eNB. So, in 4G mobile network, the eNB performs two tasks which are performed by NodeB and RNC together in 3G (UMTS) mobile network [1].

1.1. 4G (LTE) Mobile Core Network

LTE is a telecommunication standard for wireless broadband communication. The development and evolution of LTE followed the path based on the GSM/EDGE/UMTS/HSPA/HSPA+. LTE is a registered trademark of European Telecommunication Standards Institute (ETSI). Its downlink data rate is 300 Mbit/s, and uplink rate is 75 Mbit/s. LTE provides the seamless handover support for voice and data for GSM, UMTS, and CDMA2000 technologies.

Evolved Packet Core (EPC) is an enhanced feature of LTE which provides the capability of voice calling in LTE called VoLTE or 4G calling, and it works with IP Multimedia Subsystem (IMS). VoLTE is an added advantage to provide voice calling on 4G and Wi-Fi depending upon the availability of either 4G or Wi-Fi network.

For the intertechnology handover, 2G/3G technology is extended by adding various entities including Mobility Management Entity (MME), Serving Gateway (S-GW), Packet Data Network Gateway (PDN-GW), Home Subscriber Server (HSS), and Policy and Charging Rules Function (PCRF) in the EPC of 3G/4G.

1.2. 5G Mobile Core Network

The 5G deployment can be carried out in two possible ways. First is standalone deployment in which it uses independent 5G core network with new 5G new radio (NR). This approach is not very common right now because it is not a cost-effective solution, but in future, it can replace the LTE network completely. Second is nonstandalone deployment in which operator uses the improved LTE core network along with new 5G NR. This gives a leverage to mobile operators to use their current LTE architecture. This approach is in use now a days commonly.

Along with the conventional use of mobile applications, 5G will also provide the effective solution for the cloud-based technologies and IoT applications in an optimized manner. With the technological advancements, more and more applications are cloud based, and these applications require low latency which can be efficiently supported by 5G [2]. The main contributions of this paper can be summarized as follows:

(i) We propose the handover strategy in the LTE network with different approaches which provide the programmable facilities using SDN technology

(ii) We give a simple calculation method to show and prove the handover performance obtained through the proposed model and method

(iii) To reflect the user’s preferences regarding delay and data rate to meet a higher utility, we discuss in detail the relationship between handover and performance analysis of LTE network using traditional and SDN approaches. Finally, we present the concept of an acceptable handover performance and its optimization

(iv) To defend the best handover performance, we set up the experimental procedure in Network Simulator-3 (ns-3)

The remainder of this paper is organized as follows. We introduce literature review in Section 2 and propose the SDN-based handover model in Section 3. In this research, we have focused on SDN techniques in LTE network. The SDN controller allows us to improve the performance of the handovers in different situations and conditions. In Section 4, we explore the technical improvement through the necessary experiments and results. This section also provides the analysis of the results obtained from the traditional and SDN-based proposed approaches. In Section 5, we discuss the challenges of the proposed model with the emerging SDN-based technologies. Some conclusions are given in Section 6.

2. Literature Review

According to the 3GPP, latency issues during offloading can be improved when 3GPP applies the LTE-WLAN aggregation (LWA). Paper [3] proposed the SDN based approach for LWA, named as LWA under SDN Assistance (LWA-SA), which supports to improve the latency issues during the offloading and handover issues. In this approach, LWA-SA maximizes the throughput of user equipment, and SDN initiates aggregation appropriately between LTE and an optimal wireless local area network (WLAN) access point (AP), which avoids frequent reconnections and deprived services.

Reference [4] shows that distributed coordination between colocated wireless domains using either single or multiple radio technologies (such as Wi-Fi and LTE) supports the service provider for customer infrastructure sharing. These days, much of the handover traffic is off-loaded from the cellular LTE spectrum to Wi-Fi provided by APs.

The main objective of Session Initiation Protocol (SIP) is to support the mobility management protocols to optimize the handover process between heterogeneous networks vertical handover (VHO) such as Wi-Fi. Despite the optimization techniques, handover takes more time with the SIP, but paper [5] focuses on minimizing the handover time using vertical handover architecture without SIP.

According to [6], the performance of Internet usage by using VHO between the cellular network and WLAN is evaluated. When the data rate of WLAN is more than one of cellular network, VHO enhances the throughput.

In order to improve network performance and handover delay in a mobile environment, integrating handover management with SDN would be better because the SDN concept simplifies the architecture and design of the integrated system [7]. Also, SDN introduces mechanisms to improve the connections, client’s mobility, load balancing, and Wi-Fi network management. Regarding the performance evaluation of handover association mechanisms, SDN played an important role during the integration, which includes the Mininet-Wi-Fi. In this handover mechanism, the authors measure three performance metrics, i.e., transfer (KB), jitter (ms), and packet loss (%). With an extension to Mininet as the most popular SDN emulation environment, paper [8] claims that the performance of Least-Loaded-First (LLF) is a better result than Strongest-Signal-First (SSF). But Mininet-Wi-Fi does not provide the support for LTE network.

According to [9], the authors introduced the virtualization-based seamless handover in WLAN to improve seamless mobility. Also, they proposed the converged SDN-based architecture, including WLAN and LTE networks, and designed the handover workflow, which supports to measure the seamless convergence during the seamless mobility. The paper [10] proposed a seamless handover method for heterogeneous wireless networks based on SDN architecture. In this method, the deployment of the SDN control plane supports the collection of real-time network attributes and mobility information to calculate the target network, thus reducing unnecessary handovers and decreasing the handover delay. Here, the fuzzy analytic hierarchy process (FAHP) and multipath transmission control protocol (MPTCP) is applied to select the most appropriate target network and execute seamless handover, respectively. A FAHP- and MPTCP-based seamless handover method in LTE and WLAN is proposed for multiple service scenarios. The FAHP-based network selection algorithm and MPTCP-based handover mechanism support a comprehensive decision and service continuity during handover, respectively.

The original SDN controller enhances the facilities of handover processing and simplifies the traffic between wireless interfaces on one node. The extended SDN controller proves that it provides a promising solution for handover in the heterogeneous wireless network [11]. When handover between two Wi-Fi interfaces is considered, the packet loss was better, but when a handover between Bluetooth and Wi-Fi is applied, the packet loss rate was increased up to 37.2%.

Cross-layer OpenFlow switch handover algorithm for SDWNs helps the controller to function effectively and to drop the packet loss during the handover [12]. It also minimizes the hardware complexity and controller failure scenarios occurring due to high control traffic load and/or poor link quality. Authors in [13] introduced the SDN-based Hierarchical MIPv6 (HMIPv6) as a proposed approach for improving the handover in HMIPv6 networks. The main target here is to reduce the involvement of the mobile node (MN) in the handover process while still benefiting from the layered architecture of HMIPv6.

Although paper [14] focuses on handover authentication with the SDN, the technique used for developing handover authentication is useful to 5G-based heterogeneous network (HetNet) and other networks. SDN-based 5G HetNet, which supports the large number of low-power small cells including picocell, femtocell, microcell, and other access points such as Wi-Fi, is deployed in the coverage of macrocell.

The virtual representation of the MNs (vMN) not only supports the handover process of the physical MNs but also act as proxies for delivering quick information to other closest MNs. Here, the SDN controller simplifies and manages the handover processing and procedures efficiently. Within the vMN environments, SDN controller uses this information for improving the optimized handover decisions, which influence the technology-agnostic flow mobility management mechanism for heterogeneous networks [15]. According to [16], mobility SDN (M-SDN) reduces the traffic pause time caused by a host-initiated layer-2 handover considered in the SDN-based enterprise network. Here, the authors claim that M-SDN improves handover performance, such as delay, latency, and packet loss.

The handover management framework given in [17] is known as a HuMOR, which can create, validate, and evaluate handover algorithms that preserve QoS. The authors proposed ABRAHAM, a mAchine learning Backed multimetRic proActive Handover AlgorithM. Although SDN dominates handover procedures in the HuMOR, a machine learning algorithm called ABRAHAM supports to measure all handover performance.

A mobile handover mechanism based on fuzzy logic proposed in [18] enhances user satisfaction with SDN architecture and MPTCP. To implement seamless handover and improve handover performance, MPTCP can be used. It provides better throughput, achieves smoother handover, and can be adjusted to lower energy consumption. Also, the MPTCP protocol can achieve true seamless handover and ensure the quality of service for users.

According to [19], the SDN-based mobility management (SDN-MM) scheme not only enhances handover performance but also outperforms the comparison schemes in data communication efficiency. Author shows the comparisons of different mobility management schemes for a basic understanding of the handover performance.

As an application of vehicular communication, the SDN-based handover approach in IEEE 802.11p and LTE networks improve the handover facilities efficiently. Although efficient handover techniques are available, vehicular communication needs quick handover decisions in variable environmental conditions. Both LTE and 802.11p interfaces with SDN enhance the handover processing equipped in the vehicle. As the handover process happens when the SDN controller monitors the movement of vehicles and cluster information to control the vehicular network. During the handover, network performance may be degraded; to avoid this problem, SDN keeps the transport layer connection unchanged, achieving a seamless handover [20].

In [21], a horizontal handover algorithm considered with fuzzy logic control (FLC) enhances the benefits of the smart handover scheme, which supports improving the QoS and QoE. In this scheme, FLC is implemented in the centralized SDN controller which supports seamless mobility challenge, such as reduced network complexity, granular network control, and improved scalability during the handover processing.

In [22], authors presented a handover decision algorithm based on LTE-SDN architecture which significantly improved the performance of the network with respect to handover delays as well as number of handovers. The algorithm used splits the handover procedure in two phases, i.e., preparation and execution for its better management. The results show a significant improvement range from 16% to 24% on different parameters like handover delay, number of handovers, and signaling overheads while compromising RSRQ value which decreased by 4%.

A handover delay model is presented in [23], which measures the delay due to exchange of OpenFlow-related messages in mobile SDN networks. In this model, two systems were analyzed, i.e., Priority Finite Buffering (PFB) and single shared buffer without priority (model SFB) with respect to minimal buffer capacity and total handover delay. With the help of simulated results, it was observed that model PFB performs better than the model SFB when numbers of users are significantly large.

Mobility management mechanisms play an important role in mobile networks, and it is significantly vital to address the problems related to these mechanisms, especially in highly dense and heterogeneous networks. The role of SDN is addressing these issues discussed in [24]. In this work, the authors address the problems: (1) preservation of session continuity and (2) scalability during handovers. Three variants of SDN usage were presented, and it is observed that automatic management mechanism increases the robustness of the handover, and network resource utilization is optimized.

In [25], the authors discussed device to device communication scenarios based on SDN principles. The authors observed that the signaling load is decreased significantly in unicasting as well as multicasting if SDN-based architectures are used.

A SDN-based framework is presented in [26] for cellular networks. In addition to the framework, a mechanism is also proposed for management of QoS and non-QoS user traffic. The proposed algorithm for load distribution in this work is quite efficient, and the authors claim up to 24% increase in the average QoS user downlink throughput. It is further observed that with the proposed system, the desired QoS is achieved and handles the network congestion without any substantial overhead. In [27], the solution for handover failure problem is proposed using QAFT. The algorithm presented is tested with SD-LTE-RAN framework. The authors claim that it increases the throughput by 44% and reduces the delay by 42% in prioritized handovers compared to nonprioritized handover mechanisms. Moreover, it is also capable to maintain around 80% GBR satisfaction to all networks UEs.

A machine learning-based handover technique is presented in [28] to reduce the signaling overhead during handover process. The authors proposed a handover mechanism between LTE and mmWave using machine learning algorithms to automate the procedure. Furthermore, a classification algorithm is also presented in this work which is improved version of XGBoost. This algorithm predicts handover success rate based on the channel information collected through sampling window. It was observed that XGBoost-based handover achieves better results as compared to existing KNN-based handover.

3. Proposed SDN-Based Handover Model

To analyze and manage the handover in LTE network, an SDN-based model is proposed. To study and compare the data rate change and handover delay in traditional and SDN-based LTE networks, we developed a model which performs the handover of the UE in traditional and SDN-based LTE networks. The following two scenarios A and B describe the experimentation details of the traditional and SDN-based LTE networks to perform a UE handover from one eNB to other eNB. For simulation, we used Network Simulator-3 (ns-3).

3.1. Traditional LTE Handover Scenario A

We have created traditional LTE network topology in ns-3 as shown in Figure 1 which contains the EPC; Enb1, i.e., (eNB1); Enb2, i.e. (eNB2); and a UE1. The figure shows the handover of UE1 from Enb2, i.e., (eNB2), to Enb1, i.e., (eNB1), using the random mobility model. UE1 was initially connected to Enb2, i.e., (eNB2), and used a video streaming application. UE1 starts moving from the coverage area of Enb2, i.e., (eNB2), towards Enb1, i.e., (eNB1) during its mobility. Once UE1 reaches in the overlapping coverage area of the two Enbs, i.e., (eNBs), based on the Received Single Strength Indicator (RSSI) from both the Enbs, i.e., (eNBs), UE1 performs traditional handover from Enb2, i.e., (eNB2) to Enb1, i.e., (eNB1). We noted down the change in the data rate and effect of the handover on the delay faced by the video streaming application during traditional LTE handover. Later, these results will be discussed in the results section and compared with the SDN-based LTE handover.

[figure(s) omitted; refer to PDF]

3.2. SDN-Based LTE Handover Scenario B

In experiment scenario B, a SDN Controller (OFSWITCH13) is added in the topology of scenario A as depicted in Figure 2. The Switching Gateways (S-GW) with, which both Enb1, i.e., (eNB1), and Enb2, i.e., (eNB2), were connected, are replaced with the OpenFlow-based switches Switch1 and Switch2, respectively. The same experiment of scenario A is repeated but this time the handover will be assisted or controlled by the SDN controller. For this purpose, an application is written which runs on SDN controller to assist this handover. The SDN controller collects the required information from the UE1 and Enbs, i.e., (eNBs). It includes RSSI value on UE1, load on each Enb, i.e., (eNB), number of UEs connected to each Enb, i.e., (eNB). Currently, the delay faced by the video streaming application and its data rate is recorded in a file and compared to the scenario A delay and data rate. The comparison of the results of scenarios A and B is given in the experiment result analysis section.

[figure(s) omitted; refer to PDF]

The abstract view of the proposed model workflow is shown in Figure 3 which presents how the S-GW gets its follow entries from the SDN controller whether the flow table is missed or hit. In case of hit, the packet will be sent to destination as per available flow entry, whereas in case of miss, the flow entry will be updated from the SDN controller.

[figure(s) omitted; refer to PDF]

In future work, we will use the SDN controller to assist the handover in a way that it will check which eNB is overloaded and which eNB is underloaded. It will facilitate the handover of the UE to less overloaded eNB. Throughput of the network will also be managed more properly by balancing the load on the eNBs. We can say that the SDN controller will manage the load balancing while performing the handover.

4. Experiment Results and Analysis

The graph in Figure 4 shows the delay faced by the video streaming application in scenario A while performing the traditional LTE handover. For the results presented in Figure 4, the simulation time (SimTime) is shown on $x$-axis and the delay time is taken on $y$-axis. All simulation runs presented here are taken for the time from 1 to 4 seconds (sec). It is observed that the handover started from the 2 sec and completed on 3 sec.

[figure(s) omitted; refer to PDF]

The delay is minor which is noted specifically only during the handover of the UE1 from Enb2, i.e., (eNB2) to Enb1, i.e., (eNB1). At start of the handover the delay observed was 0 sec, which was increased during the handover till $7.575\times {10}^{-8}$ secs. The net increase in the delay was $7.575\times {10}^{-8}$ secs in case of traditional LTE handover. After 3 secs, once the handover was completed, the delay is stabilized.

Figure 5 shows the delay faced by the video streaming application in scenario B while performing the SDN-based LTE handover. The vertical axis shows the delay time, and the horizontal axis shows the SimTime. The simulations for this scenario B were run from 1 to 6 secs. The SDN-based LTE handover is performed during 2 to 4 secs.

[figure(s) omitted; refer to PDF]

Initially, delay faced before the handover was very low during simulation time 1 to 2 secs, i.e., $8.892\times {10}^{-7}$ sec. While there is very minor increase in the delay during the handover which reaches to $2.789\times {10}^{-4}$ sec, the net increase in the delay during the handover was $2.779\times {10}^{-4}$ sec. Moreover, it is evident that the net increase in delay during SDN-based handover is more as compared to the traditional LTE handover. One of the possible reasons is that initially the flow table is missed due to the absence of the flow entries and S-GW contacts the SDN controller to fetch the flow entries which is not the case in traditional handover. Once the handover is completed after 4 secs, the delay starts decreasing. This is an additive advantage of using SDN technology over the traditional LTE network, which becomes stabilized even after the handover is completed. The decrease in the delay in SDN-based LTE network after the completion of handover is due to the flow table hit for subsequent packets for the flow of same application.

To summarize the above presented results, we may conclude that the reason for higher delay in SDN-based LTE handover is due to the initial flow entries absence in the flow tables of the OpenFlow switches (S-GW). When the first packet of the application flow is received on the S-GW, the flow table is missed, and the first packet is sent to the SDN controller to fetch the flow entry from the SDN controller, later for subsequent packets of the same application flow, delay will be reduced as the OpenFlow-based S-GW will have the flow entries in its flow table. So, we could say that this higher delay is incurred while fetching the flow entries from the SDN controller and added in the overall delay of the SDN-based LTE handover. Otherwise, the SDN-based handover is having less delay as compared to the traditional LTE handover overall.

The graph in Figure 6 shows the data rate on the traditional LTE network while performing the handover in scenario A. The $x$-axis represents the SimTime in secs where $y$-axis represents the data rate in kilobits per second (kbps). The simulation was run from 1 to 4 secs. The traditional handover is performed during 2 to 3 secs.

[figure(s) omitted; refer to PDF]

During simulation time 1 to 2 secs, the data rate was increasing rapidly from 8.219 kbps to 16.437 kbps. Later, when the traditional LTE handover is performed during time stamp 2 to 3 secs, the data rate was still increasing but the increase in the data rate was relatively less as compared to the initial increase. The value of the data rate increased from 16.437 kbps to 19.375 kbps. It is evident from these results that the net increase in the data rate before the handover was 8.219 kbps whereas during the handover the net increase in data rate is slow down to 2.938 kbps. Once the handover is completed at 3 sec, the data rate is further slow down with the net increase 1.523 kbps and expected to be stabilized as shown in the graph of Figure 6 at some later time point.

The graph in Figure 7 shows the data rate in SDN-based LTE handover performed in scenario B. The horizontal axis represents the SimTime in secs where vertical axis represents the data rate in kilobits per second (kbps). The simulation was run from 1 to 6 secs. The SDN-based handover is performed during 2 to 4 secs.

[figure(s) omitted; refer to PDF]

Initially, before the handover started, the data rate was increasing from 2.406 kbps to 4.812 kbps during 1 to 2 secs. The net increase during this period was 2.406 kbps. Moreover, the data rate was further increasing while the handover is being performed during simulation time 2 to 4 secs from 4.812 kbps to 9.937 kbps. The net increase during the handover is 5.125 kbps. Once the handover is completed at 4 sec, the data rate starts increasing more rapidly till 6 sec; it attained the value of 23.468 kbps. This shows that the SDN-based LTE handover has less affected the increase in the data rate during handover. There is no decrease during the SDN-based LTE handover as it was in the traditional LTE handover (see Figure 6).

The results obtained from our experiments are promising that handover technique used in the proposed model is efficient, and this model allows us to create some challenges in the future use cases.

5. Challenges of SDN-Based Handovers

The handover techniques are evolving with emerging SDN-based technologies which enchase the efficiency and sensitivity of the handover in autonomous devices such as driverless vehicles. The following challenges may be considered with our proposed results for future research directions.

(i) The SDN-Based Handovers in the Driverless Vehicles. The use of SDN configuration supports provides many improvements in automation of handover facilities [30–34]

(ii) Mobility of Drones and the Interactions. Cell selection and handover measurements for drones’ interactions depend on the efficient LTE network emerged with SDN technology. The mobility and handover measurements of drones vary with heights and altitudes because these basic parameters change the handover frequencies with varying environmental and physical conditions [35–39]

(iii) High Handover Probability. Many moving objects depend on the multiple environmental conditions which affect the accuracy of the handover measurements [40–42]

These challenges may be addressed using our proposed model without any major changes. It means legacy of the proposed techniques will be same in these challenges.

6. Conclusion

The proposed solution presented in this article is an extension of the existing work in [43] for handover management in traditional and SDN-based WLAN network. Using proposed and existing architectures, we calculated the delay and data rate in traditional and SDN-based LTE networks during the handover of a UE from one eNB to another eNB.

According to results presented in Section 4 show that the delay during the handover in the traditional LTE and SDN-based LTE networks is increased but this increment in SDN-based LTE network is less than the traditional LTE network. It is further observed from the presented results that the data rate increased more rapidly in SDN-based LTE network as compared to traditional LTE network. This indicates that even if the initial delay is added in the total delay in SDN-based LTE network due to absence of the flow entries in the flow tables of the OpenFlow switches at the beginning, but still the performance of the SDN-based LTE handover is better than the traditional LTE handover in terms of overall delay and data rate.

In future, these scenarios may be extended further by incorporating the interwireless technology handover between Wi-Fi and LTE networks in the context of handover delay and data rate. The other directions in which this work can be extended are 5G+ and 6G networks with/without the M-SDN to enhance the performance of the wireless technologies in future communications.

References

[1] Commsbrief and A Ghayas, What is the Difference between NODE B, ENODEB, and GNB, 2019. https://commsbrief.com/what-is-the-difference-between-node-b-enodeb-ng-enb-and-gnb/

[2] Commsbrief and A Ghayas, What Is a Mobile Core Network, 2019. https://commsbrief.com/what-is-a-mobile-core-network/

[3] S. Anbalagan, D. Kumar, G. Raja, W. Ejaz, A. K. Bashir, "SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks," Future Generation Computer Systems, vol. 107, pp. 898-908, DOI: 10.1016/j.future.2017.12.013, 2020.

[4] P. Karimi, W. Lehr, I. Seskar, D. Raychaudhuri, "SMAP: a scalable and distributed architecture for dynamic spectrum management," 2018 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN),DOI: 10.1109/DySPAN.2018.8610416, .

[5] J. Vijayshree, T. Palanivelu, "Vertical handover triggering between WLAN and WIMAX using sip," pp. 769-774, DOI: 10.1109/ICACCCT.2014.7019195, .

[6] S. Suboonsan, S. Pattaramalai, "Performance evaluation of vertical handover on Bangkok mass transit system," International Conference on Frontiers of Communications, Networks and Applications (ICFCNA 2014-Malaysia).,DOI: 10.1049/cp.2014.1422, .

[7] R. Bencel, K. Košt'ál, I. Kotuliak, M. Ries, "Common SDN control channel for seamless handover in 802.11," pp. 34-36, DOI: 10.1109/WD.2018.8361690, .

[8] H. T. Larasati, F. H. Ilma, B. Nuhamara, A. Mustafa, R. Hakimi, E. Mulyana, "Performance evaluation of handover association mechanisms in SDN-based wireless network," pp. 103-108, DOI: 10.1109/ICWT.2017.8284148, .

[9] L. Wang, Z. Lu, X. Wen, G. Cao, X. Xia, L. Ma, "An SDN-based seamless convergence approach of WLAN and LTE networks," pp. 944-947, DOI: 10.1109/ITNEC.2016.7560501, .

[10] H. Tong, X. Liu, C. Yin, "A FAHP and MPTCP based seamless handover method in heterogeneous SDN wireless networks," ,DOI: 10.1109/WCSP.2019.8927998, .

[11] T. Nguyen-Duc, E. Kamioka, "An extended SDN controller for handover in heterogeneous wireless network," pp. 332-337, DOI: 10.1109/APCC.2015.7412534, .

[12] Y. Reddy, D. Krishnaswamy, B. Manoj, "Cross-layer switch handover in software defined wireless networks," ,DOI: 10.1109/ANTS.2015.7413639, .

[13] S. F. Hasan, "A discussion on software-defined handovers in hierarchical MIPv6 networks," pp. 140-144, DOI: 10.1109/ICIEA.2015.7334099, .

[14] J. Cao, M. Ma, Y. Fu, H. Li, Y. Zhang, "CPPHA: capability-based privacy-protection handover authentication mechanism for SDN-based 5G HetNets," IEEE Transactions on Dependable and Secure Computing, vol. 18 no. 3, pp. 1182-1195, DOI: 10.1109/TDSC.2019.2916593, 2019.

[15] F. Meneses, C. Guimarães, D. Corujo, R. L. Aguiar, "Handover initiation comparison in virtualised SDN-based flow mobility management," pp. 404-409, DOI: 10.1109/ISCC.2018.8538696, .

[16] C. Chen, Y.-T. Lin, L.-H. Yen, M.-C. Chan, C.-C. Tseng, "Mobility management for low-latency handover in SDN-based enterprise networks," ,DOI: 10.1109/WCNC.2016.7565105, .

[17] E. Zeljković, N. Slamnik-Kriještorac, S. Latré, J. M. Marquez-Barja, "ABRAHAM: machine learning backed proactive handover algorithm using SDN," IEEE Transactions on Network and Service Management, vol. 16 no. 4, pp. 1522-1536, DOI: 10.1109/TNSM.2019.2948883, 2019.

[18] D. Yao, X. Su, B. Liu, J. Zeng, "A mobile handover mechanism based on fuzzy logic and MPTCP protocol under SDN architecture," pp. 141-146, DOI: 10.1109/ISCIT.2018.8587956, .

[19] Y. Bi, G. Han, C. Lin, M. Guizani, X. Wang, "Mobility management for intro/inter domain handover in software-defined networks," IEEE Journal on Selected Areas in Communications, vol. 37 no. 8, pp. 1739-1754, DOI: 10.1109/JSAC.2019.2927097, 2019.

[20] R. Duo, C. Wu, T. Yoshinaga, Y. Ji, "SDN-based handover approach in IEEE 802.11 p and LTE hybrid vehicular networks," pp. 1870-1875, DOI: 10.1109/SmartWorld.2018.00314, .

[21] O. Aldhaibani, F. Bouhafs, M. Makay, A. Raschellà, "An SDN-based architecture for smart handover to improve QoE in IEEE 802.11 WLANs," pp. 287-292, DOI: 10.1109/WAINA.2018.00100, .

[22] K. Ahmadi, S. P. Miralavy, M. Ghassemian, "Software-defined networking to improve handover in mobile edge networks," International Journal of Communication Systems, vol. 33 no. 14, article e4510,DOI: 10.1002/dac.4510, 2020.

[23] S. Panev, P. Latkoski, "Performance analysis of handover delay and buffer capacity in mobile OpenFlow- based networks," International Journal of Communication Systems, vol. 33 no. 15, article e4529,DOI: 10.1002/dac.4529, 2020.

[24] S. Kukliński, Y. Li, K. T. Dinh, "Handover management in SDN-based mobile networks," pp. 194-200, DOI: 10.1109/GLOCOMW.2014.7063430, .

[25] H. B. Valiveti, C. Duggineni, "Software defined device to device communication handover-latest advancements," pp. 1079-1083, DOI: 10.1109/ICICT50816.2021.9358562, .

[26] F. H. Khan, M. Portmann, "Joint QoS-control and handover optimization in backhaul aware SDN-based LTE networks," Wireless Networks, vol. 26 no. 4, pp. 2707-2729, DOI: 10.1007/s11276-019-02021-7, 2020.

[27] A. K. Rangisetti, V. Sathya, "QoS aware and fault tolerant handovers in software defined LTE networks," Wireless Networks, vol. 26 no. 6, pp. 4249-4267, DOI: 10.1007/s11276-020-02323-1, 2020.

[28] N. Nayakwadi, R. Fatima, "Automatic handover execution technique using machine learning algorithm for heterogeneous wireless networks," International Journal of Information Technology, vol. 13 no. 4, pp. 1431-1439, DOI: 10.1007/s41870-021-00627-9, 2021.

[29] M. Emran, "Wireless devices handover in SDN based Wi-Fi and LTE networks," Information Sciences and Technologies Bulletin of the ACM Slovakia, vol. 13 no. 1, 2021.

[30] C. R. Storck, E. D. Efrem, G. D. Guilherme, R. A. Mini, F. Duarte-Figueiredo, "FiVH: a solution of inter-V-cell handover decision for connected vehicles in ultra-dense 5G networks," Vehicular Communications, vol. 28, article 100307,DOI: 10.1016/j.vehcom.2020.100307, 2021.

[31] D.-T. Dao, C.-M. Huang, M.-S. Chiang, V.-T. Nguyen, "A load-considered handover control scheme for distributed mobility management (DMM) using software defined network (SDN) in the vehicular environment," pp. 70-74, DOI: 10.1109/ICCE48956.2021.9352074, .

[32] M. Cicioğlu, "Multi-criteria handover management using entropy-based SAW method for SDN-based 5G small cells," Wireless Networks, vol. 27 no. 4, pp. 2947-2959, DOI: 10.1007/s11276-021-02625-y, 2021.

[33] M. Alotaibi, A. Nayak, "Linking handover delay to load balancing in SDN-based heterogeneous networks," Computer Communications, vol. 173, pp. 170-182, DOI: 10.1016/j.comcom.2021.04.001, 2021.

[34] A. Abdulghaffar, A. Mahmoud, M. Abu-Amara, T. Sheltami, "Modeling and evaluation of software defined networking based 5G core network architecture," IEEE Access, vol. 9, pp. 10179-10198, DOI: 10.1109/ACCESS.2021.3049945, 2021.

[35] H. Tong, T. Wang, Y. Zhu, X. Liu, S. Wang, C. Yin, "Mobility-aware seamless handover with MPTCP in software-defined HetNets," IEEE Transactions on Network and Service Management, vol. 18 no. 1, pp. 498-510, DOI: 10.1109/TNSM.2021.3050627, 2021.

[36] S. Singh, D. Kedia, N. Rastogi, T. Velmurugan, P. Prakasam, "P2P mobility management for seamless handover using D2D communication in B5G wireless technology," Peer-to-Peer Networking and Applications, vol. 14 no. 4, pp. 1988-1997, DOI: 10.1007/s12083-021-01123-4, 2021.

[37] L. D. Manh, N. V. Hoai, Q. V. Khanh, "Advanced handover techniques in 5G LTE-A networks," International Journal, vol. 9 no. 3, 2021.

[38] A. Fakhreddine, C. Bettstetter, S. Hayat, R. Muzaffar, D. Emini, "Handover challenges for cellular-connected drones," Proceedings of the 5th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, .

[39] J. Angjo, I. Shayea, M. Ergen, H. Mohamad, A. Alhammadi, Y. I. Daradkeh, "Handover management of drones in future mobile networks: 6G technologies," IEEE Access, vol. 9, pp. 12803-12823, DOI: 10.1109/ACCESS.2021.3051097, 2021.

[40] S. M. Kala, V. Sathya, E. Yamatsuta, H. Yamaguchi, T. Higashino, "Operator data driven cell-selection in LTE-LAA coexistence networks," International Conference on Distributed Computing and Networking 2021, pp. 206-214, .

[41] W. Huang, H. Zhang, M. Zhou, "Analysis of handover probability based on equivalent model for 3D UAV networks," ,DOI: 10.1109/PIMRC.2019.8904320, .

[42] G. Gaur, T. Velmurugan, P. Prakasam, S. Nandakumar, "Application specific thresholding scheme for handover reduction in 5G ultra dense networks," Telecommunication Systems, vol. 76 no. 1, pp. 97-113, DOI: 10.1007/s11235-020-00701-w, 2021.

[43] M. Emran, I. Kotuliak, "Performance analysis of traditional and SDN based handovers in wireless LAN networks," ,DOI: 10.1109/IEMTRONICS51293.2020.9216435, .