Handover Management for Drone Networks: Comparison
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The drone, also known as an Unmanned Aerial Vehicle (UAV), is an autonomously flying aircraft controlled by an individual. The terms drone and UAV are used interchangeably here. Drones have attracted extensive attention for their environmental, civil, and military applications. Because of their low cost and flexibility in deployment, drones with communication capabilities are expected to play key important roles in Fifth Generation (5G), Sixth Generation (6G) mobile networks, and beyond. 6G and 5G are intended to be a full-coverage network capable of providing ubiquitous connections for space, air, ground, and underwater applications.

  • drone
  • drone network
  • connected drone
  • Unmanned Aerial Vehicle (UAV)
  • handover decision algorithm
  • handover management

1. Introduction

The drone, also known as an Unmanned Aerial Vehicle (UAV), is an autonomously flying aircraft controlled by an individual. In this rpapesearchr, the terms drone and UAV are used interchangeably. Drones offer benefits such as low-cost access, effortless data collection, high efficiency, fewer hazards to humans, and logistical support. Based on their potential applications, drones can be classified as civil, environmental, or military. Drones have a wide range of civil applications, including search and rescue operations for missing people, aerial photography, construction, recreation, inspection of electric power lines, manufacturing, transportation, logistic deliveries, crowd monitoring, surveillance, mining, and archaeology. One important application of drones is the delivery of medical supplies and medications in emergency cases. Drones are also useful in environmental sectors such as wildlife protection, crop monitoring, pollution control, mountain inspection, and land and water surveillance [1][2]. Drones are also used in scientific investigations, such as oceanic and cyclone monitoring in areas that are unreachable to humans. Drones were first used for military activities such as intelligence gathering, spying, military surveillance, and object tracking, but they have since also been used for civilian and environmental purposes. In the military sector, drones are applied in war zones, to combat aircraft, spying, border surveillance, attack and missile launching, and other use cases. There are numerous drone applications with diverse needs and goals, making it difficult to categorize aerial networks into specific application domains. Further detailed discussions on practical applications and case studies of drones can be found in [3][4][5][6][7][8][9][10][11]. Moreover, numerous Fifth Generation (5G)-related applications are emerging with the development of the new cellular technology, as indicated by 3GPP [12][13][14].
Drones have been recently included as User Equipment (UE) in the cellular architecture. The control link contains two major components: a point-to-point connection between the drone and the person maneuvering it, and a link that establishes a cellular network connection between the drone terminal and the Ground Control Station (GCS). Drones can also serve as ABSs in the sky to serve UE at specific locations. When drones are used as ABSs, they can support the connectivity of genuine terrestrial wireless networks such as broadband and cellular networks. The advantage of using drones as ABSs compared to conventional ground stations is their capability to alter their height, avoid obstacles, and improve the probability of creating Line-of-Sight (LoS) communication links for terrestrial users. Due to their unique properties such as flexibility, mobility, and adaptive altitude, Drone Base Stations (DBSs), can efficiently complement current cellular systems by providing supplementary capacity for hotspot locations. They can also offer network coverage in unreachable rural areas. Multiple linked drones can be used in certain situations where a single drone is incapable of delivering services provided by the drone network.
Another significant application of drones is their integration with the Internet of Things (IoT) [14][15][16][17][18][19]. IoT devices typically have low transmit power and may not be able to communicate over long ranges. Drones can also be used in surveillance scenarios, which is a key requirement for IoT. In cities or countries where towers and complete cellular infrastructure are expensive, drone deployment will become extremely beneficial since it eliminates the need for such costs. The conventional cellular architecture may be significantly altered to enable the application of drones in different service scenarios.
Various field tests have been conducted by several communication companies such as AT&T, China Mobile, Ericsson, ZTE, LG, Nokia, and Qualcomm [20][21][22][23][24]. Due to spectrum availability concerns, current investigations are underway using Wi-Fi, 802.15.4, and remote-control channels [10][25][26]. Other existing technologies have also been analyzed for wireless drone support such as 802.11, 802.15.4, Third Generation (3G)/Long-Term Evolution (LTE), and infrared. The authors in [27] examined the issue of drone interference in the context of adopting drone communications in the cellular infrastructure. Cell coverage and drone support have also been explored in the literature. However, extensive studies are still required.
Despite the potential prospects of drones, a range of practical challenges must be overcome to effectively apply them in each networking application. For instance, when using drone BSs, the most critical aspects to consider are performance characterization, drone implementation in optimal Three-Dimensional (3D) environments, wireless and computational resource management, flight time, trajectory optimization, and network planning. Handling channel modeling, low-latency control, 3D localization, and interference management are also key challenges in the connected drone concept. Among these challenges, efficient mobility (handover) management is a significant factor that must be addressed for drone BSs and drone UE scenarios [28]. To ensure smooth and reliable connection services while users are mobile, a secure connection must be established in addition to an efficient handover process.
Handover technology is the method of maintaining a continuous connection when a user moves from one cell to another without disrupting service. Serving signal level reduction, load balancing, and high error rates are among the factors that lead to the formation of handover actions. When one or more of the aforementioned factors reach an undesirable level, the connection must transfer to a suitable alternative cell for more reliable, stable, and seamless service. Although this process regularly occurs, it creates many challenges when the UE is a drone.
Several challenges must be overcome to manage handovers in mobile networks. System complexity increases with drone implementation due to their unique features. The drone’s flight may be controlled via LoS paths, even though the interference scale is greater than that in conventional terrestrial networks. Compared to the ground UE, the drone UE has a lower coverage probability since its antenna is tilted downward and the drone’s interference is overpowered by LoS [29][30]. Due to the higher speed of drones compared to that of the ground UE, the handover rate is comparatively higher. Since drones are supported by the sidelobe of the terrestrial antenna, many handovers will probably occur [31]. Consequently, the Quality of Service (QoS) will noticeably degrade [32].
Handover of drones must be professionally and expertly managed in terms of the techniques used to address handover challenges compared to current handover management in terrestrial UEs. Techniques and algorithms employed in terrestrial UEs may not be suitable for drone network applications due to their distinctive features. The key objective for using such methods is to deliver high-quality service and reliable communication while maintaining seamless handover between drones. Solutions have been investigated in several related works, but many challenges still remain. The provided algorithms are for both scenarios: drones acting as BSs and drones serving as UEs. The former scenario is under examination using the previously suggested algorithms. Drone BSs are assessed in two separate movement scenarios: drone BSs travelling in random directions at the same constant speed and drone BSs moving at various constant speeds.
In future mobile networks, node movement prediction is a key recommended technique for enhancing drone network service. Many contemporary methods are based on distance measurements and projections. Machine learning-assisted studies have been developed to support drone networks in acquiring certain patterns. This will enhance the performance of handover management, such as in [32][33].

2. Handover Management for Drone Networks

Drones will serve various environments and be a significant part of future mobile networks. However, handover management will be a critical matter that must be addressed in future networks. Accordingly, this section highlights handover management in drone networks.

2.1. Handover in Drone Networks

The handover performance is a common assessment in cellular networks since it is a good indicator for demonstrating the efficient mobility techniques. Handover, or handoff, is a key technique in mobile networks that allows a UE to switch its connection across BSs while on the move. Handover with drone networks has become a more significant matter because the connected drones move in the sky faster with different characterizations. Depending on the functionality of drones within the network, one or several drones may be needed to provide network access services to specific terrestrial users. Drones may also serve as UEs and receive service from ground BSs or from satellite networks. Since a drone’s operation is restricted by its power, coverage, mobility characterizations, and serving network traffic, handover will be increasingly required. The handover (handoff) process is crucial for the continuation of a connection, imposing only a short delay [34]. Furthermore, the drone network remains highly dynamic since mobile aerial vehicles and the radio environment are different compared to ground users due to several factors, such as high altitude [26][35]. The traditional handover control systems in MANETs and VANETs must be altered to be suitable for drone networks. In MANETs, the commonly utilized handover techniques lead to constantly separating or merging network nodes [36]. Several architectures for drone traffic control systems have been proposed. For instance, NASA and the Federal Aviation Administration (FAA) proposed the UTM scheme [37]. The European Union is also developing U-space, which contains a set of guidelines and services [38].

2.2. Handover Decision Algorithms

A variety of handover decision-making algorithms are used in cellular networks, such as RSRP, Received Signal Strength Indicator (RSSI) of the Serving Base Station (S-BS), the Signal-to-Interference-Plus-Noise Ratio (SINR), mobile movement speed, distance between the UE and BS, limited capacity of BSs, weight functions, cost functions, fuzzy logic control, and machine with deep learning technology. The same handover decision algorithms can be used with drones, but the performance will differ due to the different characterization of drones [39][40][41][42][43][44][45][46]. Moreover, the requirements of 6G technology will be ultra-high compared to those of the previous mobile systems. This also creates the need for more robust, efficient, dynamic, and smart handover decision algorithms for drones’ networks. Several studies have been conducted in the literature that deal with this matter.
For example, the authors in [43] created a method for establishing drone connectivity with IoT. The model architecture consists of two main nodes: the sensor node and the data processing node. Two different modes of communication are utilized: Wi-Fi and satellite communication. The handoff was performed based on several parameters: network accessibility, RSSI, QoS, cost of data transmission, and network performance. If one of the previous criteria indicated that the Wi-Fi interface is not the optimal choice, vertical handover is performed to switch to the satellite communication mode. If neither of the interfaces correctly operate, buffering is then performed to avoid packet loss until one of the interfaces becomes available.
The authors in [23] investigated a method that analyzes the impact of heterogeneous movement Device-to-Device (D2D) drone-supported Mission-Critical Machine-Type Communication (mcMTC) in 5G. Due to the rapid increase in the use of IoT systems, mcMTC’s role has become extremely significant. Therefore, fulfilling these extensive requirements is necessary. The respapearchr examined the influence of various movement patterns on heterogeneous users. The researchstudy verified that, as long as alternative connectivity options are in use, availability will increase. The WINTERsim simulator was applied for the evaluation.
The impact of a heterogeneous device’s movement is based on the multi-connectivity options, which introduce three measured cases: vehicular connection, manufacturing automation, and city communications. The UEs included in the multi-connectivity system can utilize D2D, cellular, and drone-supported connections. Ref. [44] proved that low and limited mobility of the device has no effect on the connection availability and reliability. Since the packet sizes are diverse, the use of D2D-assisted communications and drones greatly enhances reliability and data rates. In contrast, performance degradation was detected for cases where movement was high.

2.3. Handover Types

Handover in cellular networks can be classified into different types, based on technique, network type, network management, operating frequency, and scenario. For example, handover can be classified into two main handover technique types: hard and soft handover techniques. The hard handover requires the UE to terminate the connection from the serving BS before it switches to the target BS. The soft handover imposes a more gradual connection termination, simultaneously maintaining a connection with two or more BSs for a short period of time [45]. The drones’ network can apply two different handover techniques depending on the mobile communication technology.
Handover also can be classified into different types based on the technology of the serving and target networks. The two main types are horizontal handover and vertical handover. In the horizontal handover, the access points use the same technology and the network interface remains unchanged. In vertical handover, the access technologies are different from each other, and multiple network interfaces are employed. For instance, the user switches from the terrestrial cellular network to satellite technology, as illustrated in Figure 1.
/media/item_content/202209/63193dc233bd1sensors-22-06424-g004.png
Figure 1. Handover scenarios with connected drones in future mobile networks.
Furthermore, handover in cellular networks can be classified into three methods depending on the network management system: (i) Network-Controlled Handoff (NCHO), (ii) Mobile-Assisted Handoff (MAHO), or (iii) Mobile-Controlled Handoff (MCHO) [46][47]. The handover control system is extensively described in [48]. For example, if the recipient signal is the mechanism triggering parameter, two handover scenarios will occur: absolute or relative. The former occurs when the serving BS signal strength becomes lower than a pre-defined threshold value, whereas the latter occurs when the serving RSRP is lower than that of the target BS. The relative handover technique may cause handover to occur earlier than needed yet provides higher quality. Absolute handover, however, causes what is referred to as the “ping-pong” effect. This phenomenon occurs from frequent variations in the RSRP value, prompting frequent handovers. These various handover types can also be applied with drones’ networks.

2.4. Handover Procedure in 5G

The handover procedure is a significant process that consists of different steps, algorithms, and techniques to enable UEs to switch connections from one cell to another. The procedural steps differ from one technology to another. The same procedure used for the terrestrial UE can work with drones; however, it does not guarantee efficient handover performance since the characterization of drones is different. This subsection provides a brief description of the handover procedure for one handover system scenario that may occur, as illustrated in Figure 2 (as an example).
/media/item_content/202209/63193e0eef8b7sensors-22-06424-g005.png
Figure 2. S1 key renewal process in drone networks.
The 5G handover process is closely similar to LTE-Advanced system with some further enhancements. The Access and Mobility Management Function (AMF) conducts the responsibility of the Mobility Management Entity (MME) [49]. The User Plane Function (UPF) is the same as the Serving Gateway (SGW). The handover procedures are listed as follows:
  • The UE periodically sends the measurement report to the S-BS.
  • The S-BS configures the measurement procedure of the UE.
  • Based on the measurement report, the S-BS makes the switch decision, and the handover request is then received by the Target Base Station (T-BS).
  • The T-BS replies with an acknowledgment to the S-BS based on its resources.
  • The handover is initiated, and the T-BS supplies the UE with the necessary information, connecting it to the target cell.
  • The UE receives uplink allocation and timing info sent from the T-BS.
  • The T-BS updates the AMF for UE cell alteration, the UPF is updated by the AMF for the UE, the path of the UE is updated by the UPF, then the AMF notifies the T-BS for path update.
  • The S-BS is updated by the T-BS for the completion of the handover.
Another way of categorizing handover is based on whether the UE controls or assists in the process. A handover in which both the network and the UE are involved is known as a hybrid handover. These categories have been investigated for mobile Internet Protocol (IP) networks and VANETs, but only a few studies are currently available for drone networks.

References

  1. Li, X.; Savkin, A.V. Networked unmanned aerial vehicles for surveillance and monitoring: A survey. Future Internet 2021, 13, 174.
  2. Bajracharya, R.; Shrestha, R.; Kim, S.; Jung, H. 6G NR-U based wireless infrastructure UAV: Standardization, opportunities, challenges and future scopes. IEEE Access 2022, 10, 30536–30555.
  3. Zeng, Y.; Zhang, R.; Lim, T.J. Wireless communications with unmanned aerial vehicles: Opportunities and challenges. IEEE Commun. Mag. 2016, 54, 36–42.
  4. Akram, R.N.; Markantonakis, K.; Mayes, K.; Habachi, O.; Sauveron, D.; Steyven, A.; Chaumette, S. Security, privacy and safety evaluation of dynamic and static fleets of drones. In Proceedings of the 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), St. Petersburg, FL, USA, 17–21 September 2017; IEEE: Piscataway, NJ, USA, 2017.
  5. Mozaffari, M.; Saad, W.; Bennis, M.; Nam, Y.-H.; Debbah, M. A tutorial on UAVs for wireless networks: Applications, chal-lenges, and open problems. IEEE Commun. Surv. Tutor. 2019, 21, 2334–2360.
  6. Asadpour, M.; Bergh, B.V.D.; Giustiniano, D.; Hummel, K.A.; Pollin, S.; Plattner, B. Micro aerial vehicle networks: An experimental analysis of challenges and opportunities. IEEE Commun. Mag. 2014, 52, 141–149.
  7. Marcus, M.J. Spectrum policy challenges of UAV/drones . IEEE Wirel. Commun. 2014, 21, 8–9.
  8. Elmeseiry, N.; Alshaer, N.; Ismail, T. A detailed survey and future directions of unmanned aerial vehicles (UAVs) with po-tential applications. Aerospace 2021, 8, 363.
  9. Jiang, X.; Sheng, M.; Zhao, N.; Xing, C.; Lu, W.; Wang, X. Green UAV communications for 6G: A survey. Chin. J. Aeronaut. 2021, 35, 19–34.
  10. Hayat, S.; Yanmaz, E.; Muzaffar, R. Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint. IEEE Commun. Surv. Tutor. 2016, 18, 2624–2661.
  11. Aggarwal, S.; Kumar, N.; Tanwar, S. Blockchain-envisioned UAV communication using 6G networks: Open issues, use cases, and future directions. IEEE Internet Things J. 2020, 8, 5416–5441.
  12. Muruganathan, S.D.; Lin, X.; Maattanen, H.-L.; Sedin, J.; Zou, Z.; Hapsari, W.A.; Yasukawa, S. An overview of 3GPP release-15 study on enhanced LTE support for connected drones. IEEE Commun. Stand. Mag. 2021, 5, 140–146.
  13. Popescu, D.; Dragana, C.; Stoican, F.; Ichim, L.; Stamatescu, G. A collaborative UAV-WSN network for monitoring large areas. Sensors 2018, 18, 4202.
  14. Al-Fuqaha, A.; Guizani, M.; Mohammadi, M.; Aledhari, M.; Ayyash, M. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 2015, 17, 2347–2376.
  15. Park, T.; Abuzainab, N.; Saad, W. Learning How to Communicate in the Internet of Things: Finite resources and heterogeneity. IEEE Access 2016, 4, 7063–7073.
  16. Zanella, A.; Bui, N.; Castellani, A.; Vangelista, L.; Zorzi, M. Internet of things for smart cities. IEEE Internet Things J. 2014, 1, 22–32.
  17. Ferdowsi, A.; Saad, W. Deep learning-based dynamic watermarking for secure signal authentication in the internet of things. In Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 20–24 May 2018; IEEE: Piscataway, NJ, USA, 2018.
  18. Ding, G.; Wu, Q.; Zhang, L.; Lin, Y.; Tsiftsis, T.A.; Yao, Y.-D. An amateur drone surveillance system based on the cognitive internet of things. IEEE Commun. Mag. 2018, 56, 29–35.
  19. Rodrigues, L.; Riker, A.; Ribeiro, M.; Both, C.; Sousa, F.; Moreira, W.; Cardoso, K.; Oliveira-Jr, A. Flight planning optimization of multiple UAVs for internet of things. Sensors 2021, 21, 7735.
  20. Crawley, E.F. Intelligent structures for aerospace-A technology overview and assessment. AIAA J. 1994, 32, 1689–1699.
  21. Sundqvist, L. Cellular Controlled Drone Experiment: Evaluation of Network Requirements. Master’s Thesis, Aalto University School of Electrical Engineering, Espoo, Finland, 2015.
  22. Lefebure, M. Device for Piloting a Drone. U.S. Patent 8214088B2, 3 July 2012.
  23. Orsino, A.; Ometov, A.; Fodor, G.; Moltchanov, D.; Militano, L.; Andreev, S.; Yilmay, O.N.C.; Tirronen, T.; Torsner, J.; Araniti, G.; et al. Effects of heterogeneous mobility on D2D-and drone-assisted mission-critical MTC in 5G. IEEE Commun. Mag. 2017, 55, 79–87.
  24. Al-Hourani, A.; Gomez, K. Modeling cellular-to-UAV path-loss for suburban environments. IEEE Wirel. Commun. Lett. 2017, 7, 82–85.
  25. Van den Bergh, B.; Vermeulen, T.; Pollin, S. Analysis of harmful interference to and from aerial IEEE 802.11 systems. In Proceedings of the First Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use, Florence, Italy, 18 May 2015.
  26. Gupta, L.; Jain, R.; Vaszkun, G. Survey of Important Issues in UAV Communication Networks. IEEE Commun. Surv. Tutor. 2015, 18, 1123–1152.
  27. Van Der Bergh, B.; Chiumento, A.; Pollin, S. LTE in the sky: Trading off propagation benefits with interference costs for aerial nodes. IEEE Commun. Mag. 2016, 54, 44–50.
  28. Banagar, M.; Chetlur, V.V.; Dhillon, H.S. Handover Probability in Drone Cellular Networks. IEEE Wirel. Commun. Lett. 2020, 9, 933–937.
  29. Amer, R.; Saad, W.; Marchettic, N. Mobility in the sky: Performance and mobility analysis for cellular-connected UAVs. IEEE Trans. Commun. 2020, 68, 3229–3246.
  30. Angjo, J.; Shayea, I.; Ergen, M.; Mohamad, H.; Alhammadi, A.; Daradkeh, Y.I. Handover management of drones in future mobile networks: 6G technologies. IEEE Access 2021, 9, 12803–12823.
  31. Zeng, Y.; Lyu, J.; Zhang, R. Cellular-connected UAV: Potential, challenges, and promising technologies. IEEE Wirel. Commun. 2018, 26, 120–127.
  32. Azari, A.; Ghavimi, F.; Ozger, M.; Jantti, R.; Cavdar, C. Machine learning assisted handover and resource management for cellular connected drones. In Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 25–28 May 2020; IEEE: Piscataway, NJ, USA, 2020.
  33. Yang, H.; Hu, B.; Wang, L. A deep learning based handover mechanism for UAV networks. In Proceedings of the 2017 20th International Symposium on Wireless Personal Multimedia Communications (WPMC), Bali, Indonesia, 17–20 December 2017; IEEE: Piscataway, NJ, USA, 2017.
  34. Ohleger, M.; Xie, G.G.; Gibson, J.H. Extending uav video dissemination via seamless handover: A proof of concept evaluation of the IEEE 802.21 standard. In Proceedings of the 2013 46th Hawaii International Conference on System Sciences, Wailea, HI, USA, 7–10 January 2013; IEEE: Piscataway, NJ, USA, 2013.
  35. Fakhreddine, A.; Bettstetter, C.; Hayat, S.; Muzaffar, R.; Emini, D. Handover challenges for cellular-connected drones. In Proceedings of the 5th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, Seoul, Korea, 21 June 2019.
  36. Hu, B.; Yang, H.; Wang, L.; Chen, S. A trajectory prediction based intelligent handover control method in UAV cellular networks. China Commun. 2019, 16, 1–14.
  37. Federal Aviation Administration. Unmanned Aircraft System (UAS) Traffic Management (UTM); NextGen Concept Operations; Federal Aviation Administration: Washington, DC, USA, 2018; pp. 1–68.
  38. SESAR Joint Undertaking. U-Space Blueprint; SESAR Joint Undertaking: Brussels, Belgium, 2017; Volume 18.
  39. Jang, Y.; Raza, S.M.; Kim, M.; Choo, H. Proactive handover decision for UAVs with deep reinforcement learning. Sensors 2022, 22, 1200.
  40. Gupta, A.K.; Goel, V.; Garg, R.R.; Thirupurasundari, D.R.; Verma, A.; Sain, M. A fuzzy based handover decision scheme for mobile devices using predictive model. Electronics 2021, 10, 2016.
  41. Mollel, M.S.; Abubakar, A.I.; Ozturk, M.; Kaijage, S.; Kisangiri, M.; Zoha, A.; Imran, M.A.; Abbasi, Q.H. Intelligent handover decision scheme using double deep reinforcement learning. Phys. Commun. 2020, 42, 101133.
  42. Hussain, S.M.; Yusof, K.M.; Asuncion, R. Artificial intelligence based handover decision and network selection in heteroge-neous internet of vehicles. Indones. J. Electr. Eng. Comput. Sci. 2021, 22, 1124–1134.
  43. Gaur, A.S.; Budakoti, J.; Lung, C.H.; Redmond, A. IoT-equipped UAV communications with seamless vertical handover. In Proceedings of the 2017 IEEE Conference on Dependable and Secure Computing, Taipei, Taiwan, 7–10 August 2017; IEEE: Piscataway, NJ, USA, 2017.
  44. Jung, S.; Kim, J. A new way of extending network coverage: Relay-assisted D2D communications in 3GPP. ICT Express 2016, 2, 117–121.
  45. Ergen, M. Mobile Broadband: Including WiMAX and LTE; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2009.
  46. Akyildiz, I.; McNair, J.; Ho, J.; Uzunalioglu, H.; Wang, W. Mobility management in next-generation wireless systems. Proc. IEEE 1999, 87, 1347–1384.
  47. Tripathi, N.D.; Reed, J.H.; VanLandinoham, H.F. Handoff in cellular systems. IEEE Pers. Commun. 1998, 5, 26–37.
  48. Lampropoulos, G.; Passas, N.; Merakos, L.; Kaloxylos, A. Handover management architectures in integrated WLAN/cellular networks. IEEE Commun. Surv. Tutor. 2005, 7, 30–44.
  49. Isa, I.N.; Baba, M.D.; Ab Rahman, R.; Yusof, A.L. Self-organizing network based handover mechanism for LTE networks. In Proceedings of the 2015 International Conference on Computer, Communications, and Control Technology (I4CT), Kuching, Malaysia, 21–23 April 2015; IEEE: Piscataway, NJ, USA, 2015.
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