Performance evaluation of TCP and UDP during LTE handover

Zhang, Liang; Okamawari, Takao; Fujii, Teruya

doi:10.1109/wcnc.2012.6214116

Cited by 17 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although there is also a researcher who make comparisons between the two algorithms [10]. Furthermore, most studies are based on simulations, generally using NS3 simulator, though there are also some researchers using other simulation tools such as Matlab [11]- [12], or other simulation languages such as C ++ [13]. Some researchers also conducted research related to other aspects of the effect of handover on LTE.…”

Section: A2mentioning

confidence: 99%

See 1 more Smart Citation

Performance Evaluation of A2-A4-RSRQ and A3-RSRP Handover Algorithms in LTE Network

Hendrawan¹,

Zain²,

Lestari

2019

indones.j.electron.telecommun.

View full text Add to dashboard Cite

In LTE Network, users can move freely in the network through fast and seamless handover (HO). This research focuses on intra-LTE handover which occurs using interface X2 to move an EU between two eNBs, i.e. source eNB and target eNB without any changes in MME and SGW at EPC level. Two popular algorithms of intra-LTE handover namely A2-A4-RSRQ and A3-RSRP were evaluated and compared through simulations as well as direct measurements in the field. Simulation is conducted using NS3 simulation tool where performances of various scenarios from both algorithms were evaluated. The performance metrics studied include the average number of HOs that occur, throughput and optimized ratio. Simulations carried out for various scenarios in term of EU numbers, user speeds, and channel conditions. In addition, the results of one-month measurement of three eNBs were also presented. The measurement results are then compared and used to verify the simulation results. Furthermore, by using the optimizing ratio metric, the optimal pair of parameter values of Threshold as well as Offset and Handover Margin (HOM) along with Time-to-Trigger (TTT) are sought for the A2-A4-RSRQ and A3-RSRP respectively.

show abstract

Section: A2mentioning

confidence: 99%

“…Some researchers also conducted research related to other aspects of the effect of handover on LTE. For example, [12] evaluates the TCP and UDP transmission performance due to the effects of handover implementation on the LTE network. While other researcher conducted a study related to handover on High Speed Railway system [14].…”

Section: A2mentioning

confidence: 99%

Performance Evaluation of A2-A4-RSRQ and A3-RSRP Handover Algorithms in LTE Network

Hendrawan¹,

Zain²,

Lestari

2019

indones.j.electron.telecommun.

View full text Add to dashboard Cite

show abstract

“…The authors have tried to reduce the retransmissions to improve performance. Zhang et al (2012) TCP overall performance is performed and the cross layer design is actually proposed to improve performance. To adaptively command retransmission conclusion between sender along with receiver, control package size along with packet volume a feedback details about the funnel state is employed.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Handover in WiMax with TCP and UDP as Underlying Protocol

Mayoof¹,

Hassan

Ahmed³

et al. 2015

Journal of Computer Science

View full text Add to dashboard Cite

The rejection of an ongoing connection/session is serious problem and it degrades the QoS and efficiency of the network rather than rejecting a request for new connection. The impact on performance of handovers is a serious problem in cellular systems that must be addressed. During handover there may be delay in packets and connections may drop. Real time applications i.e., VoIP and streaming video can be adversely affected by such kind of delays. In this study we used TCP and UDP as an underlying protocol for exchange of data between two wireless mobile nodes in a WiMax access network and had evaluated the effect on performance in terms of end-to-end delay and throughput. The results showed that when handover is triggered the transfer window resets to zero causing higher throughput and end-to-end delay for TCP than UDP based on packet size and traffic load.

show abstract

“…Each node represents a random variable. The random variable s e t ) is the hidden state at time t (in the model of (1) (2) With these assumptions, the joint probability distribution of all variables can be simplified as follow: Several inference problems can be addressed on top of instantiated HMM [15], which are also basically concerned in mobility prediction. One of them is that given a model /L = {A, B,7r} , how to efficiently compute the likelihood of an output sequence P( 0\0200•o ,I /L) .…”

Section: B Hidden Markov Modelmentioning

confidence: 99%

“…The throughput and delay perfonnance are demanded to satisfy certain criteria even in the mobility scenario. Specifically, in EPS Connection Management (ECM)-CONNECTED state, the mobility of User Equipment CUE) is handled by the handover procedure [1] during which the performance of TCP and UDP is degraded due to the interference between source eNodeB and target eNodeB [2]. [n order to improve communication condition and provide service of better perfonnance, researchers have paid attention to mobility pattern and prediction of individual mobility trajectory.…”

mentioning

confidence: 99%

Hidden Markov Model based user mobility analysis in LTE network

Mei

Qiao

et al. 2014

2014 International Symposium on Wireless Personal Multimedia Communications (WPMC)

View full text Add to dashboard Cite

Models of human mobility have broad application in fields such as mobile computing, network planning and resource preservation. In L TE network, the rapid increasing number of mobile broadband users requires the availability of enhanced data services and efficient mobility management. This paper focuses on service evolved Node B (eNodeB) prediction in LTE cellular network based on human mobility, and proposes a theoretical and factual method leveraging Hidden Markov Model (HMM). The usage of HMM allows us to consider trajectory characteristics of eNodeB accessed as unobservable parameters, and also the effects of individual's historical service eNodeBs. We experiment with factual data that derives from the real-world cellular network, and analyze how different parameters impact the prediction performance using control variate method. The result shows a prediction accuracy of 53% can be achieved. These findings are very significant for the location prediction problem. And the model exhibits more merit if adopted in factual communication system. Keywords-L TE; user mobility; evolved Node B predication; Hidden Markov Mode; control variate method I.[NTRODUCTION With lower latency and higher bandwidth than its predecessor 30 Network, L TE has an expected fast-growing user base in China. The throughput and delay perfonnance are demanded to satisfy certain criteria even in the mobility scenario. Specifically, in EPS Connection Management (ECM)-CONNECTED state, the mobility of User Equipment CUE) is handled by the handover procedure [1] during which the performance of TCP and UDP is degraded due to the interference between source eNodeB and target eNodeB [2]. [n order to improve communication condition and provide service of better perfonnance, researchers have paid attention to mobility pattern and prediction of individual mobility trajectory. Since individuals usually have a precise purpose or habitual route, human mobility behavior is far from random, and is highly influenced by their historical behavior. Thus, mobility trajectory based location predication is promising to obtain high accuracy to guarantee resource preservation and better service performance. This paper addresses the development and evaluation of a model that supports the analysis and inference of a statistical pattern for next service eNodeB predication. The pattern captures the sequential relations between accessed eNodeBs in a given time period for particular individuals. In this paper, Hidden Markov Model is adopted for prediction. [n this model, we account trajectory characteristics of eNodeBs accessed as unobservable parameters, and also consider the effect of each individual's historical service eNodeBs. Based on the real world dataset of province-wide cellular network, we evaluate the prediction performance of the introduced model under different parameters with control variate method. And we analyze how different parameters impact prediction performance. The overall obtained result corresponds to an accuracy of 53%. The method used and the re...

show abstract

Performance evaluation of TCP and UDP during LTE handover

Cited by 17 publications

References 12 publications

Performance Evaluation of A2-A4-RSRQ and A3-RSRP Handover Algorithms in LTE Network

Performance Evaluation of A2-A4-RSRQ and A3-RSRP Handover Algorithms in LTE Network

Performance Evaluation of Handover in WiMax with TCP and UDP as Underlying Protocol

Hidden Markov Model based user mobility analysis in LTE network

Contact Info

Product

Resources

About