Cellular Handover approaches in 2.5G to 5G Technology

Khan, Jahangir; Abbas, Ali; khan, Khisro

doi:10.5120/2482-2984

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…Authors used more than 6000 cells of real networks for successfully identifying the separating handover behaviours, forecasting future number of handover attempts and detecting abnormal handover behaviours of cells. In [7] this paper authors Teodora Sandra Buda and Haytham Assem explained about the limitations of current network and service management and completely explains the challenges that 5G faces from management perspective. The crisp thing of this paper is it presents number of use cases and scenario of 5G in machine learning which can address their management challenges.…”

Section: Related Workmentioning

confidence: 99%

Handover forecasting in 5G using machine learning

Kumar¹,

Vanmathi²,

Sanjay³

et al. 2018

IJET

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Communication plays a major role in human's life. Without network communication can't be done, to achieve proper and effective communication different generations of networks are introduced. Each generation has its own features and perspective of communication, but till now there is no network properly makes people to communicate. Many researches says 5G network will rule the network world as it satisfies all the effective network goals. This paper is proposed to obtain all the goals of a communication network by making proper handover with the help of machine learning. Here we have used two main algorithms to make our 5G handover process by clustering and classifying. Clustering is a process of making the datasets into single units of every users and classification is a process of classifying user's clustered datasets into common path using prediction and forecasting. For clustering we are using K-means and for classification we are using Random Forest algorithm. By using the algorithms the datasets which is being predicted and forecasted is stored in the cloud. Here cloud technology is used as a platform for developing datasets associated with internet. 5G network adapts to any form technology easier and here we have used all the essential technologies under machine learning. This paper deals with all the above methodologies effectively with newer combinations of algorithms along with proper solutions.

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Section: Related Workmentioning

confidence: 99%

Handover forecasting in 5G using machine learning

Kumar¹,

Vanmathi²,

Sanjay³

et al. 2018

IJET

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“…The Third Generation Partnership Project (3GPP) [2] [3], has developed the foremost winning customary technologies within the mobile communication market like Universal Mobile Telecommunication System (UMTS) and future Long Term Evolution (LTE). It is presently polishing off the standardization of each Fifth Generation access network system and Fifth Generation core network system at identical time.…”

Section: Introductionmentioning

confidence: 99%

Mobility Management for 5G Mobile Networks

Sapkale¹,

Kolekar²

2018

IJCA

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The current design and standardization of the next generation or Fifth Generation(5G) will enable new used cases, applications and impressive challenging requirements in terms of mobility performance. For example, next generation mobile networks should support seamless mobility with zero data interruption at each handover, even at high speeds. This work proposes a new research analysis of Mobility Management(MM) solutions in cellular network for next generation. Conventionally Mobility Management solutions were developed for LTE but, from last few years due to increase in demand for high speed seamless mobility without interruption for every handover, the solutions remained incompetent. Hence for improvement in QoS and reducing the delay,this proposed work method will involve the reduction of delay during handoff and analysis for mobility management in both Low speed and High speed scenarios. However,the measurements also reveal that the handover data interruption time can sometimes be hundreds of milliseconds and would try to fulfill the next generation demands. Studies of mobility are conducted for a variety of environments,including generic scenarios with hexagonal network topologies, non uniform site specific scenarios, pedestrian mobility and high speed. So using different network architectures we would implement dual connectivity as well.

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“…The Five parameters are modeled statistically, with the terrain information on, road width and roof top height, with height of base station, distance from base station. The model is validated by comparing the simulated results with the measurement campaigns carried out in urban and suburban regions [10]- [11]. From the path loss model the RSS has been estimated which gives a better performance.…”

Section: Introductionmentioning

confidence: 99%

“…Also the RSS hugely depends on pre-set environmental information [5]- [6]. The hand off in wireless communications are proposed mainly based on the parameters of RSS, signal to noise ratio (SNR), interference and bit error rate [11]- [13]. A number of VHOS are studied where the scheme proposes VHOS between either of two networks [3]- [6] taking various complex methods.…”

Section: Introductionmentioning

confidence: 99%

Received Signal Strength Based Vertical Hand Off Scheme for K-Tier Heterogeneous Networks

Panda

Patra

Acharya

2013

2013 International Conference on Communication Systems and Network Technologies

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The k-tier heterogeneous wireless networks (kHWN) deployment focused on the needs of the users. Many challenges exist in integrating kHWN architectures into one seamless flow of voice, data and multimedia. The handoff's between kHWN that allows user equipment (UE) to continuously find seamless connectivity with the transceivers is complex and challenging. The common and most desired, cost effective parameter for vertical handoff scheme (VHOS) is the receiving signal strength (RSS). In this paper we investigated the best suited empirical and statistical model to get the optimized result of the RSS for GSM, UMTS, advanced Long Term Evolution (LTE) and WLAN which are integrated in the kHWN. Based on the RSS results the VHOS algorithm for kHWN is proposed, to achive mobility within the kHWN. The simulation results validate the findings.

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Cellular Handover approaches in 2.5G to 5G Technology

Cited by 12 publications

References 17 publications

Handover forecasting in 5G using machine learning

Handover forecasting in 5G using machine learning

Mobility Management for 5G Mobile Networks

Received Signal Strength Based Vertical Hand Off Scheme for K-Tier Heterogeneous Networks

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