An Efficient Architecture in Next
Generation Wireless Networks
Anjali Rasal*, Dr. S. D. Lokhande** Dept. of Electronics and Telecommunication Engg.
Sinhgad College of Engineering, Pune, India
ABSTRACT
Next-Generation wireless networks are proposed to achieve the goal of ubiquitous wireless networking by utilizing heterogeneous networks. The performance of Location management scheme directly affects the overall performance of NGWN at large. This affect significantly impacts on the signalling traffic in terms of the database’s waiting time, query and update response time, processing power, bandwidth requirements etc. in the global roaming. Several architectures have been proposed to reduce such traffic. We have analyzed the impact of different system parameters with different architectures, one is multi-tier HLR and another is WING architecture. The performance of these architectures is analyzed with parameters such as location management cost, paging cost, query response time etc. The result of this work shows that WING architecture enhances the location management scheme than MHLR architecture during heterogeneous system roaming process. Thus in global roaming WING architecture can improve the network performance significantly in terms of signalling traffic.
Keywords: Wireless Interworking Gateway (WING), Multi-tier HLR (MHLR), Next-Generation Wireless
Networks (NGWN).
1. INTRODUCTION
The foreseeable deployment of next-generation (NG) wireless systems, e.g., International Mobile Telecommunications 2000 (IMT-2000) and 4G systems will lead to an enormous increase in both the number of mobile subscribers and mobile applications. The distinguished features of NG wireless systems can be highlighted as quality of service (QoS) provisioning for various applications and global roaming. The demand to provide 4G services to an increasing population of mobile users has placed new requirements on wireless systems. The mobile users require that QoS constraints be maintained throughout the duration of an application, even though they roam not only from cell to cell but also from one system to another with different technologies. It embraces all the interconnected communication networks across different country’s borders. This roaming feature is referred as global roaming. Such type of roaming is supported by the mobility management in the network. Roaming and mobility are two folds of one thing. It is the motivation of this work to select architecture from suggested schemes, which reduces signalling traffic loads in international roaming situation [1], [2]. Future generation wireless networks are envisioned to be a combination of diverse but complementary access technologies. Interworking these types of networks will provide mobile users with ubiquitous connectivity across a wide range of networking. The integration existing and emerging heterogeneous wireless networks requires the design of intelligent handoff and location management schemes to enable mobile users to switch network access and experience uninterrupted service continuity. The performance of location management scheme directly affects the overall performance of NGWN very large. The objective of our work is to determine an efficient and effective scheme which reduces the overhead of tracing and finding a mobile user. So we have selected location management parameters for this analysis.
This paper is organized as follows. In section I, we explained impact of location management process in next generation mobile networks and objective of this paper. In next section we covered different architectures which can be employed for location management in NGWN. In section III, we discussed about parameter selection and its significance in traffic analysis. Section IV deals with assumptions and data considered for analysis. Numerical results and its comparison are covered in section V and finally we conclude the paper.
2. MATERIALSANDMETHODS
Classification of Location Management Architectures
Fig.1 Architecture with MHLR for Global Roaming Management
With MHLR two registration strategies are proposed, Single Registration (SR) and Multiple Registration (MR). SR method enables the mobile terminal to register with the MHLR with only one subsystem at any time to indicate its current location. MR method network allows the mobile terminal to register with MHLR on multiple subsystems at any time concurrently. In this work we have analysed performance MHLR architecture with these both methods of registration [2] [3].
Wireless Interworking Gateway (WING) architecture is second architecture which we have selected for comparison. In this architecture a special gateway is used called as WING, between pair of adjacent networks in order to reduce the signalling traffic at databases (HLRs, VLRs). Thus traffic is distributed through different gateways. Interworking can be plugged into the existing infrastructure to implement and manage the heterogeneous subsystem mobility. WING also implements traffic monitoring function in its traffic management function by discarding the packets from unauthorized users. Fig. 2 shows interconnection of WING with HLRs for two subsystems, subsystem i and j. The WING is also acts like a converter modules which ensures the conversion of signalling message formats and perform protocol translation from subsystem i to subsystem j or vice versa[2] [3] [1].
Fig. 2 Interconnection of WING with HLRs for Two Subsystem Network
Parameter Selection in Traffic Analysis
The efficient architecture should satisfy following conditions in global roaming analysis.
1. Low location management rate: When a mobile user frequently crosses the boundaries of adjacent location
areas, the location management rate should not increase significantly.
2. Low location management cost: When the procedure of location management is required the location management cost in terms of bandwidth should be as low as possible.
We will select an architecture which helps to improve above requirements. In the selection of parameters for this analysis we have also considered following set of operations which are performed in location management scheme. To establish the amount of signalling traffic generated during global roaming the three set of operations are executed in location management.
Location Registration Process
(b) Query to HLRj (c) Updating HLRj
(d) Query to VLRjx (i.e. the VLR which controls LAjx) (e) Updating VLRjx
(f) Updating HLRi
(g) Updating VLRix (i.e. the VLR which controls LAix)
Location Update Process
The MT needs to update its current location within the visited subsystem j periodically and the set of operations in location update process is given below [2].
(a) Query to VLRjx (b) Query to HLRj (c) Updating HLRj (d) Updating WINGij (e) Updating VLRjx (f) Updating HLRi (g) Updating VLRix
Call delivery Process:
Operations for call origination (i.e. outgoing traffic) and call delivery (i.e. incoming traffic) must be added in the set of operations and the steps are as follows [2].
(a) Query to WINGij (b) Query to HLRi (c) Query to VLRix
If the MT has moved to subsystem j (a) Query to HLRj
(b) Query to VLRjx (c) Paging the MT.
From above considerations we have decided the parameters for analysis, such as query response time, update response time at various databases, WING and MHLR to estimate location management rate and its impact on the signalling traffic. Also to analyse bandwidth usage the location management cost, paging cost and location registration cost parameters are decided.
Performance Analysis
For intersystem roaming two subsystems i and j are considered for performance analysis. In order to evaluate the impact of various parameters we define following parameters.
NVLRi/j Number of VLRs in subsystem i/j
Ρi/j User Density of subsystem i/j
Vi/j Average speed in LAix/jx, x=1,2,…. NVLRi/j Ai Total Area in subsystem i/j
λouti/j Average traffic generated by each MT of subsystem i/j(Calls/second/terminal) Li/j Length of boundary of subsystem i/j
Rij/ji Rate of users moving out subsystem i/j to subsystem j/i
λini/j Average incoming traffic to each MT of subsystem i/j(Calls/second/terminal)
Table 1: Data for Performance Analysis
Parameters for Analysis Proposed Value
Probability for data cached at VLR 0 to 1/NVLR Query arrival rate at VLR 0.075 queries per ms Update arrival rate at VLR 0.075 queries per ms
Query processing time at VLR 10ms
Update processing time at VLR 20ms
Query processing time at HLR 10ms
Update processing time at HLR 30ms Transmission time for a message of an avg.
70bytes with 64kbps
8.75ms
3. RESULTSANDDISCUSSION
The architecture performances are analysed for different set of parameters Evaluations of mobile user behaviour such as movement in the location area, crossing the boundary of location area (LA), average speed of mobile terminal are considered for the determination of query and update rate. We evolved different parameters for each method SR, MR and WING and compared the results in this analysis to determine efficient architecture.
Query and Update Rate
The impact of query response time and update response time is analysed with global call to mobility ratio (GCMR). Mobile user behaviour can be characterized by using parameter GCMR. GCMR is the ratio of the average call rate to an MU over the number of times per second this MU executes an intersystem roaming. We considered GCMRij/ji parameter to include average number of calls originating from subsystem i to j/j to i. Fig.3 (a) and (b) shows response time of query and update at WING and MHLR (for both SR and MR method) with GCMR.
(a)
(b)
Fig.4 (a) Response of Update Rate at WING and MHLR (b) Response of Query Rate at WING and MHLR
In figure 3 (a) and (b) blue coloured response represents WING and other two for MHLR. From analysis it shows that WING is at least 64% better than MHLR architecture, also for higher value of GCMR i.e. low intersystem roaming rate, WING performance is 99 % better than SR and MR algorithm. The result of fig. 3 (b) reveals that query rate performance of WING architecture is better by 78% with MR method and with SR method around 82% for lowest value of GCMR i.e. highest value of rate of change of mobility. When number of
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WING MR SR
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users performing intersystem roaming becomes less then there is no traffic load in the network. So in such case also WING architecture is better by 80% than MHLR architecture.
We have also analysed query and update rate at VLR and HLR. Fig. 4 (a) and (b) show impact of query and update rates with rate of change of mobility (Rij/ Rji) at VLR and HLR. It proves that update rate at HLR database is improved by proposed approach by 75% than MR method at highest or lowest traffic load in heterogeneous roaming. WING is advantageous by 96% than SR scheme in any condition of traffic load. In the performance evaluation of query rate at HLR in intersystem roaming, the response of WING architecture exceeds largely put to around 75% than both SR and MR method in all cases considered for mobile user behaviour. Therefore WING is best in all conditions of traffic analysis for query and update rate evaluation.
(a) (b)
Fig. 4 (a) Update Rate at HLR (b) Query Rate at HLR
As shown in above fig. 5 we can study the response of average response delay for query at databases with the change in user density.
Fig. 5 Impact of User Density on Response Delay
The result of this experimentation tells us that, for lower user density response time of MHLR architecture is better than WING. But when number of mobile users in a subsystem increases performance of the network starts to degrade i.e. delay increases. In case of WING architecture since at lower user density response delay is high, but with increase in accumulation of mobile users rate of change in delay remains almost constant or does not degraded.
Evaluation of Signalling Cost
In this work we have also measured signalling performance with location management scheme for both architectures. The overall location management cost (LMC) usually depends upon the procedures used to
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WING MR SR
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WING MR SR
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process the location registration messages and to generate and broadcast the paging messages. The term ‘cost’ can be reflecting to the bandwidth usage and processing power required. So the evaluation of LMC per unit time is shown in equation 1. LRRi represents location registration rate for subsystem i and PRi indicates paging rate for i [5] [4].
. (1)
Where is a scaling factor to adjust the relative cost of paging a MT in a single cell.
Figure 6 shows response of location management cost with waiting time where Location management cost is weighted sum of location registration rate and paging cost. In the figure 6, the LMC is lower with waiting time for proposed approach WING than SR and MR method in MHLR architecture.
Fig. 6 Response of Location ManagementCost with Waiting Time
4. CONCLUSIONS
In this paper the performance of different parameters for different architectures is analysed. Various tools NS2, OPNET such real time simulators can be used for traffic analysis in mobile networks. Here we have chosen MATLAB due to availability and flexibility for application development.
In this work the performance of WING architecture is compared with MHLR architecture with its two approaches, SR and MR methods. The traffic analysis results for WING architecture are compared with MHLR architecture. The results shows that the query and update responses can be reduced significantly in WING architecture. It also improves response delay regarding the set of operations in location management. Location management cost need to be considered for selection of best sub network. Location registration cost, paging cost and location management costs are also analysed in this work for each network, which concludes that WING architecture can give better performance than both SR and MR method of MHLR in location management scheme.
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