A History-Based Handover Prediction for LTE Systems

Huaining Ge Xiangming Wen Wei Zheng Zhaoming Lu Bo Wang School of Communication & Information Engineering,

Beijing University of Post and Telecommunications (BUPT) Beijing, P.R. China, 100876

ghncg2008@163.com xiangmw@bupt.edu.cn zhengweius@gmail.com lzy_0372@163.com wangbo5629587@gmail.com

Abstract—Handover prediction has been considered an effective technology for improvement of LTE Handover performance. Although not a few techniques have been proposed to achieve this goal including handover preparation based on cross-layer optimization and mobility prediction, the fact is that their gains are not usually so high as their cost. To overcome such weaknesses, this paper proposes a simple handover prediction technique which is based on a novel user mobility model to approximate simulation the laws of user mobility actions. We develop a user mobility database to assist the mobility prediction based on the user mobility history records. The simulation results show that the proposed technique minimizes the number of handovers and lower the ping-pang rate in LTE system.

Keywords-LTE; Hnadover prediction; user mobility database

I. INTRODUCTION

As LTE system is currently under extensively study, the user mobility management is of special interest. Mobility of users with seamless accessibility and without the need to care about the underlying topology is the very source of many challenging issues. There are several management tasks that are deeply influenced by the user’s mobility. To guarantee a seamless service the correct estimation of user’s future location is critical. Mobility prediction has been considered as an effective technique for fast and seamless handovers.

Different research approaches try to use movement predictions as an addition to classical handover preparation and triggers. They vary from statistical analysis [1, 2] up to handover preparation based on cross-layer optimization [34]and complex pattern detection algorithms [5]. However, the weaknesses of those mobility techniques result from their insufficient cost to performance ratio. A simple handover prediction approach is proposed in [6], in which simple moving average for inertial movements and simple mobility pattern matching non-inertial movements. However, almost all works about mobility prediction ignore the fact that users’ movements are not completely random. Especially in the case of cellular networks this information could be used to optimize handover algorithms. In this paper, a typical urban scenario is considered where the lives of the majority of the residents of the existence of strong regularity; performance in the mobile often with a strong purpose. With the Characteristics of the user’s mobile of the historical records and the purpose-space point of the mobile

users and with a curve connecting the purpose-space point, we propose a novel mobility prediction approach, i.e. history-based handover prediction. This approach is useful to improve the accuracy of a prediction of user’s location and optimize the handover configuration parameters.

The rest of the paper is organized as follows: In section 2 the approach of the proposed technique is described. Section 3 introduces the handover technique details while section 4 deals with model implementation and simulation results. And finally in section5 the work is concluded.

II. THE APPROACH OF THE PROPOSEDTECHNIQUE

A. Key assumptions The Key assumptions for the proposed mobility model and

its analysis are listed as follows.

• For urban users, whose mobile region only include three points (home workspace and hot) .Hot is the existence region except home and workspace abstract of all existence region (e.g., supermarket, park, square, hospital, etc.). (In this paper, home, workspace and hots are covered by a single cell )

• Between the two points which belong to the finite point space set (home, workspace and hot), exist not only a path for the mobile users. Each path has a different weight and markers; and the weight of the path is proportional to the probability numbers of theUsers appearing in the path. However, (In this paper, we take the path in which the user appears that the greatest probability, i.e. There is only one path between any two points.)

• A point which belongs to the finite point space doesn’t appear in the middle of the path; the user mobility model describes the law between the two points, and each path across a number of cells of LTE system and the cells have isometric radius.

The simplicity mobile model is shown in Fig.1. In a period of time, all of the user mobility actions include to the regular and irregular action. First let denote the proportion of user mobility in regular terms. A high value of (i.e. >0.7)

This work was supported by Beijing Municipal Commission of Science and Technology and Beijing University of Post and Telecommunications under 4G projects (Do8o100620802) and supported by National Natural Science Foundation of china(60872050).

978-1-4244-5273-6/09/$26.00 ©2009 IEEE

Figure 1. The simplicity user mobility model

Figure 2. The process the database system

implies that a high proportion of users mobility is in regular, whereas a low value of (i.e. <0.3) corresponds to users mobility in irregular. If user mobility rule set denotes:

( )1

i N

route ii

P θ=

=

= )(irouteP is the probability of a UE to

employ the route (i). According to the records of the user in database .B , network determines the current pattern of the user is regular or no-regular. If the result of the judgment is regular, the model is valid, i.e. Handover prediction has been simplified and Network performance has been improved. On the country, the model is invalid.

B. Personalized mobile user database In the system, the network should recognize the users who

frequently visit to a cell, which we call their “user-friendly” for this cell. And, the network should track and record the movement information of the “user-friendly”. The MME Calculate on the records, and extracts the user's mobile law at intervals in a period. In this paper, we establish a database to utilize the user’s mobile law for each user, which is used to record the user's mobile behavior rules. The Process of the system is shown in Fig.2.

Initial Records of the database is taken from a table which is finished by the user, when he/she enroll into the networks. The table should include the user's home address and the user's working conditions and his/her preferences, and then according to these information, the approximate laws of the mobile user can be extracted for mobile users database initialization. However, the data denotes the rule of the user’s mobility just only network guess on the basis of the information which the user submit, not the rules of the user in real life. Consequently, network should track and record the user’s locomotion activity in a relatively long period, and use the records to optimize the database. Table 1 shows the records of the mobility behavior of a user in real life. And with the records further processing, the results of the processing consist of the basic data of the user mobility database (is shown in table 2). Hot-A (Hot-B, Hot-

TABLE I. USER MOBILITY ORIGINAL RECORD

TABLE II. USER MOBILITY DATABASE

C…etc.) denotes the Cell-ID of the cell in which the user often appears. The probability of the user appearing in and the user detach the route, are represented as and , respectively. In this context, )(irouteP=α .The results of optimization should to the greatest extent simulate the rules of the user mobility behavior in his/her everyday life.

C. The method to update the database The different law is shown by the user’s moving at

intervals, and the network updates the database when necessary. The network automatically updates data (add or clear the route and cells) at regular intervals or by unscheduled event trigger. Fig.3 illustrates how the database updating functionality works.

III. THE HANDOVER TECHNIQUE DETAILS

A. The user’s mobility pattern to decide The completion of the user’s mobility pattern to decide

action is in the side of the network; it doesn’t need interaction between the side of the user and network. e.g.; the information of the reselection or handover can include the locating information of the previous cell. According to the information and the location of the user, network can make use of the lookup table to search a route to match the route of the current user from the database. The user’s mobility pattern to decide the process is represented in Fig.4.

The user’s movement action starts from some a point which belongs to {home, workspace and hot}, after N cells, and ends up with another point. In the process, the results of the table lookup consist of the optional set {route (i)}, and the next cell in each route consists of Potential handover set

{ }( ( ))nextHO HO route iP p= .

User-ID Cell-ID Update-Cycle Friendly-Rank

The number of the user appear in the cell n

Average Cell dwell time

The number of handover of the user in the cell

The number of Cell reselection of the user in the cell

The number of handover or reselection from each

icell (i=1,2……8).(The icell is the neighbor of the cell)

User ID Home Cell-ID Workspace Cell-ID Hot-A Hot-B Hot-C Hot-D Hot-E

Router(i) Start cell End cell

Figure 3. user mobility database update process

Figure 4. user mobility pattern to decide

B. UE Measurement and Measurement Reports A UE manages the following information to help an

eNodeB predict the handover of each UE: potential handover set and handover candidates. Potential handover set

}{ ))(( nextirouteHOHO PP = of a UE is an ordered set including the cells which is the result the match in .A. Handover candidate set based on signal strength }{ )()( iSSHOssHO CC = of a UE is an ordered set including n cells whose signal strength measured value that UE is greater than a certain threshold actTh . The

received energy chip divided by the power density of a cell ic ,denoted by )(/ 0 ic cNE , of the common pilot channel (CPICH) is used as the signal strength.

New reporting events are introduced to send measurement information for handover prediction as follows. Reporting events A1 and A2 occur when the CPICH 0/ NEc of the

current cell becomes smaller than a certain threshold alertThand actTh for the first time; respectively. An eNoteB does not inform the UE to start the measurement, record oc NE / and

periodic send the report each time for HOP until it receives a measurement report caused by an event A1 from that UE. If the reporting event A2 occurs, eNoteB starts comparing the record,

Figure 5. New Events for Measurement Reports

e.g. From event A1 occuring to event A2, UE measurement total 5 times. The value of 0cE N is 1v , 2v , 3v , 4v and 5v ,respectively. According to the comparative result decide the event 1D. Reporting event 1D occurs when the comparative result is gradually decreased, and the CPICH awayc ThNE <0/

(of HOP ).Reporting event A3 occurs when the CPICH

0cE N < 1preTh of the current cell and the CPICH

0cE N > 2preTh (of the HOb ), and eNoteB start handover preparation. Fig.5 depicts a situation where new events occur according to UE’s movement.

C. Handover prediction and preparation in eNodeBs An eNodeB decides the best handover candidate for a UE,

denoted by HOb , using the measurement reports received from

that UE. HOb can be decided as follows. First, if the current move pattern is regular and for the

nextirouteHOP ))(( reporting

event 1D doesn’t occur, contemporary a cell ic such

that issHOirouteHO cCPnext

==1)())(( and in the report the cell

CPICH 20/ prec ThNE > , where 2preTh is the threshold of

signal for handovers preparation to start, ic is assigned to HOb ;

and if all1)())(( ssHOirouteHO CP ≠ , and a cell ic such as

]])([)(

)1[(max1)(0

)(0)(

1

ssHOc

ssHOciroute CNE

CNEPMax ⋅+⋅− ββ and

ic is assigned to HOb . If the current move pattern is regular

and reporting event 1D occurs for allnextirouteHOP ))(( , then

HOssHO bC =1)( .When HOb is decided, and the handover

preparation is performed between the source eNodeB and the target eNodeB corresponding to HOb .It may include the handover admission control, and the context and radio resource information exchange for pre-synchronization. And, the eNodeB controls the handover execution time with the current and the succeeding measurement reports.

Otherwise, when the user mobility pattern is random, the handover prediction model is invalid. In this case, the users

moving terms is random. Early works [6] proposed many valid methods, and this paper offers some useful supplementary methods.

IV. NUMERICAL RESULTS AND DISCUSSIONS

There are two sides used to measure the performance of the proposed technique which are known as the number of handovers and the rate of Ping-pang handovers. And we first tested the fast handover procedure with the proposed technique. Then for performance evaluation of the proposed technique we also implemented LTE system without the proposed technique and made a comparison between two systems with Tau performance simulator [7].

A. simulation Models The simulation model of the cellular network area consists

of 28 macrocells that have the same radius of 1km. The signal level the UE received from eNodeBi , )(0 ic cNE , is calculated

as ii Nd +⋅⋅−⋅ )200/(log21016log10 1010 , where id is the

distance between the UE and ieNodeB , and iN is the sum of zero-mean gaussian background noise and interference noise. We choose six UEs with the different mobility laws to test the proposed technique; detail parameters as shown in Table 3.

simulation results As for the number of handovers, the proposed technique

was about 10% better than that of the handover without the

TABLE III. USER MOBILITY LAW’S PARAMENTERS

Figure 6. the number of handovers

Figure 7. the Pingpang rate

proposed technique when the hysteresis threshold is small as shown in Fig.6. The more regularity of user mobility, the greater performance gain has been gotten with the proposed technique as expected. At = 0.9 and = 0.05, the proposed technique has gotten 30% performance gain. But, at = 0.5 and

= 0.2 the proposed technique obtains almost no any performance gain. This is because the proposed technique is only for the greater regularity mobility.

The rate of ping-pong handovers shows similar results. In Fig.7, we fixed the time condition fixed 60s for ping-pong handover; if the stochastic of user mobility was increased, the rate of ping-pong handovers was also increased as expected. The handover with the proposed technique showed lower rate of ping-pong handovers for every ping-pong handover condition. This result shows that the proposed technique is also useful to reduce unnecessary and ping-pong handovers owing to the correct handover prediction.

V. CONCLUDING REMARKS

This paper proposed a handover optimum approach with simple handover prediction based on acts of users of mobile history. The proposed prediction technique uses the user mobility database and simple mobility pattern matching as well. A valid update database approach is proposed, to ensure the database time in tracking the user mobility actions. The numbers shows that the proposed technique has a smaller number of handovers and lower rate of ping-pang handover than the handover process without the proposed technique. The comparison with other prediction techniques and further comprehensive performance analysis on the handover preparation are now in progress for refining the proposed technique.

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[3] J. McNair,et al., “A survey of cross-layer performance enhancements for Mobile IP networks”, Computer Networks, Volume 49, Issue 2, Elsevier,2005,pp.119–146.

[4] J. Jung et al., “Mobility Prediction Handover Using User Mobility Patternand Guard Channel Assignment Scheme”, LNCS, Vol.3264, 2004.

[5] Poon, W.T.; Chan, E.” Traffic Management in Wireless ATM Network Using a Hierarchical Neural-Network Based Prediction Algorithm,” Proceedings of the International Conference on Computers and their Applications, ICSA ,2000.

[6] Tae-Hyong; Qiping Yang; Jae-Hyoung Lee; Soon-Gi Park;Yeon-Seung Shin;”A Mobility Management Technique with simple Handover Prediction for 3G LTE Systems” Vehicular Technology Conference,2007.VTC-2007 IEEE 66th,2007,pp 259-263.

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UE UE1 UE2 UE3 UE4 UE5 UE6 0.5 0.5 0.7 0.7 0.9 0.9 0.3 0.25 0.2 0.15 0.1 0.05