A DECISIVE BEST EFFORT SERVICE ALGORITHM DESIGNED TO RATIONALIZE CALL FAILURES IN CELLULAR NETWORK

International Journal of Emerging Technology and Advanced Engineering

544

A DECISIVE BEST EFFORT SERVICE ALGORITHM

DESIGNED TO RATIONALIZE CALL FAILURES IN

CELLULAR NETWORK

S. Malathy

, T. Ravichandran

1_{Research Scholar, Anna University, Tamilnadu, India}

2_{Principal, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, India}

Abstract - The paper proposes an admission control scheme based on adaptive bandwidth reservation to provide QOS guarantees for multimedia traffic carried in high-speed wireless cellular networks. The proposed scheme allocates bandwidth to a connection in the cell where the connection request originates and reserves bandwidth in all neighboring cells. It combines admission The proposed scheme adjusts the amount of reserved bandwidth based on the current network conditions. It distinguishes new calls and hand-off calls. It gives higher priority to hand-off calls to provide higher connectivity to users who are already accepted in the network. It reduces the bandwidth assigned to non-real time calls (i.e., low priority calls) to provide higher quality of service to real time calls (i.e., high priority calls), if necessary. The proposed scheme is performed at each base station in a distributed manner. The amount of bandwidth reserved is adjusted dynamically.

Keywords - Handoff calls, bandwidth reservation, Call admission control, Mobile Networks.

I. INTRODUCTION

The wireless cellular network experiences a tremendous increase in the number of mobile users in the last decade. It is a challenging task for the service provider to allocate resources to the users due to the limited bandwidth. So the network is expected to be capable of providing a promised Quality of Service (QoS) to different services like as video, multimedia and data. This becomes more crucial when the traffic generated by the users is more than the capacity of the system. Mobility management and bandwidth management are two major research issues in a wireless cellular network. Mobility management consists of two basic components: Location Management and Handoff Management. The work in this thesis demonstrates the different types of admission control and channel allocation schemes to reduce the new call and handoff call failures. It is more important to provide support to the ongoing call than allowing the new call into the network. The admission control decides whether to accept or deny the incoming call to a cell and the bandwidth management algorithm allocates the available resources for the different types of calls in a network.

The key feature of this analysis is to minimize the new call blocking rate and handoff call dropping rate. By minimizing the call failures the available bandwidth is efficiently managed. In this research a superior handoff processing technique with priority assignment is proposed to meet the QoS performance parameters.

In this thesis, first a Dynamic Channel Allocation Algorithm is addressed. The main advantage of theDCA algorithm lies in allocating the bandwidth for the calls based on the traffic. The insufficient resource experienced by a mobile user is rectified here. DCA efficiently reuses the channels and improves the performance of the system.

This framework also shows the dynamic reservation of bandwidth for handoff calls based on the prediction of the mobile users‟ next cell movement. Here the prediction is made for both regular users and irregular users by examining the behavior of the mobile user time and duration of the call.

The CAC investigates dynamic priority queuing of handover calls in wireless networks. The proposed framework is anticipated to be a very useful tool in evaluating performance of the network. The handoff call is buffered if the required resource is not available. Higher priority is given to handoff calls in order to reduce their forced termination. Since the calls are queued instead of providing dedicated guard channels to handoff calls, the number of channels available for new calls increases

New simulation algorithm developed here tackles the problem related with maximizing the revenue for the mobile service provider. This dissertation tries to maximize the revenue of the service provider via CAC. Integrating pricing and CAC not only generates income for the service provider thereby reducing the congestion in the network.

International Journal of Emerging Technology and Advanced Engineering

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But this becomes inefficient when proper resources are not available. The idea proposed here achieves a best QoS and the handoff call is never dropped so that an annoying situation for the mobile user is avoided. This seems to be beneficial for both service providers and mobile users. The mobile users try to maintain the online connection, when they move from one wireless system to another.

For evaluating the network performance, a deep investigation is made on CAC algorithms and Bandwidth management schemes. The main focus of this research is to identify the some QoS issues which affect the overall performance of mobile network. After a detailed study, it is shown that the proposed scheme reduces the new call blocking probabilities, the handoff dropping probabilities and reduces significantly the probability of terminating calls while still maintaining efficient bandwidth usage.

II. LITERATURE SURVEY

The need to evolve a nonconventional Biological system that provides a better solution through their intelligent optimization techniques is explained (Abdullah et. al., 2011). It also describes the need to apply swarm intelligence to develop new location prediction that it will improve the percentage of correct predictions of node movements as compared to other conventionally used mobility models in MANETs as these mobility models.

A three layer Neural Network System with 8 hidden neurons was used to develop the model for mobile movement prediction and multiple movements of mobile nodes (Partha Pratim Bhattacharya, Manidipa Bhattacharya, 2011). The proposed method for mobile movement prediction is based on the Mobile Node‟s history of movement patterns, which has been recorded for certain time duration. Here two mobiles nodes with regular and random movement patterns have been considered for prediction.

A theoretical and factual method to perform mobility prediction in cellular network using Hidden Markov Model is propose and analyzed (Hongbo SI et. al., 2010). The major parameters concerned were prediction accuracy and time consumed and the inspection was made on two different actual environments. The first examination was made for new users who haven‟t visited the location are and the second was the user who has accessed the location area frequently providing the database for modeling.

In the development of location-based services, (Sang-Jun Han and Sung-Bae Cho, 2006) the user‟s movement trajectory was modeled using a combination of recurrent self-organizing maps and the Markov model.

Future movement is predicted based on past movement trajectories. A database called Reality Mining Project from MIT media laboratory was used for mobility prediction, a particular location area with group of cells is chosen as the background of research.

An analytical model to compute the Blocking Probability for linear highway and circular cellular systems with centralized and distributed dynamic channel allocation is devised . Here (S. Anand et. al., 2003) 18-cell linear highway system with centralized and distributed dynamic channel allocation and 61-cell circular cellular system with distributed dynamic channel allocation is experimented without setting any priority to ongoing call.

A mathematical framework for voice communication systems is derived (Ozan K. Tonguz, and Evs¸en Yanmaz, 2008). These expressions were derived for different load balancing schemes like Simple Borrowing, Directed Retry, Channel Borrowing without Locking, Mobile-Assisted Call Admission Algorithm. The expressions derived here can be used to compute the performance of wireless networks employing dynamic load balancing in seconds as opposed to hours which helps the service providers to adjust the system parameters such as the number of load-balancing channels, etc., according to the traffic conditions rapidly. Considered Two CAC polices namely reservation and threshold and through numerical experiments it was shown that the search structured policy algorithms converge quickly and work for systems with large capacity and many call classes (Tsang, D. H. K., Tatikonda, S. , Bensaou, B. ,2007). In addition, a comparison between reservation policy and threshold policy were experimented and it was proved reservation policy is more fair and robust when compared to threshold policies.

An analytical model applicable for dynamic channel allocation scheme for channelized cellular mobile circuit-switched systems that support general arbitrary distributed handoff traffic has been proposed (Bhattacharya, S,Gupta, H.M, Kar, S, 2008). The objective this model was to design an analytical model with DCA to compute congestion for all kinds of traffic streams. The proposed model has been simplified for DCA under restricted mobility conditions.

International Journal of Emerging Technology and Advanced Engineering

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A novel simulation model was designed to predict the bandwidth reservation by incorporating road layouts that place constraints on mobile users‟ travel path (Wee-Seng Soh, Hyong Kim, 2006). The possibilities of positioning errors have been studied using simple error model. It is also proved that when the traffic load varies, the dynamic bandwidth reservation varies to meet the desired Handoff dropping probabilities. The predictions were done for a time interval 0f 5s and 10s.

A priority based resource sharing scheme for cellular networks supporting voice and data services using Markovian analysis was analyzed. Two cases of CAC are simulated with lognormally and exponentially distributed data call length respectively (Chi Wa Leong, 2006). Here Load adaption and bandwidth allocation polices are proposed for adaptive CAC to match the variations in traffic variations in a cellular environment.

A new admission control policy for wireless mobile multimedia networks that combines call admission control with buffer to prevent congestion in a mobile wireless network is being proposed (Ojesanmi O. A, Ojesanmi A. and Makinde O., 2009). The new real-time and non-real-time calls or real-time and non-real-time handoff calls are analysed here. Hot spot calls are given priority over cold spot calls if the cold spot request is non-real-time. Call of the same cell and priority are served using ticket scheduling. The system is modelled by a multi-dimensional Markov chain.

Analyzed A priority queuing scheme that gives priority in the same class of calls according to their Total Resource Requirements (TRR) was proposed. The scheme discussed gives a higher priority to the calls that have a lower TRR over the calls that have a higher TRR. The proposed TRR-based queuing scheme is compared with the FIFO queuing scheme and the non queuing interference-based call admission control scheme (Shiquan Piao et al., 2006). The performance gain of the TRR-based queuing scheme over the FIFO scheme in the offered load of 10 Erlang is 28.4%, but in the offered load of 15 Erlang, the performance gain is increased to 41.1%

The performance of queues for handover calls in both a micro- and a macrocell is analysed (Seung-Que Lee, 2004). The role of a macrocell is as a secondary server for handover calls overflowed from microcells This is analyzed by varying the number of microcells under a macrocell, arrival rates of new calls in micro- and macrocells, cell dwell times, signal degradation interval, and queue size of both micro- and macrocells In the model, and the overflow process of handover calls from microcells is modelled by Poisson and Markov modulated Poisson process (MMPP) approximations.

An algorithm proposing an optimal CAC that maximizes the overall network revenue with QoS constraints in both WLAN and CDMA networks is devised . The utilization of radio resources in a heterogeneous integrated system consisting of two different networks (Fei Yu, Vikram Krishnamurthy, 2007).

III. PROPOSED WORK

3.1 Handoff in cellular networks

The Geographical area is divided into smaller areas in the shape of hexagon. These hexagonal areas are called as cells. A Base Station (BS) is located at each cell. The Mobile User (MU) within that region is served by this BS. When a MU moves away from its current BS, it must be reconnected to a new BS to continue its call process. Transferring the active call from one cell to another without disturbing the call is called as the process of Handoff. Handoff is implemented mostly on voice calls. Handoff process is initiated either when the mobile user crosses the cell boundary or when the received signal is poor. Two categories of handoff are hard handoff and soft handoff. Hard Handoff is “break before make” process and soft handoff is “make before break”. The handoff call is forced to terminate if no free channel is available. This process is called handoff call blocking. Setting priority to handoff calls improves the QoS of the network.

The blocking for either a new Call when no Queuing is done:

Where

N= Number of Voice Channels a = Where

= arrival rate for originating calls ( calls per sec)

= arrival rate for handoff calls ( calls per sec)

International Journal of Emerging Technology and Advanced Engineering

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The blocking probability of handoff call is:

3.2 Buffering the handoff calls with priority assignment

Handoff calls should be assigned high priority because dropping the ongoing call will be more annoying than the new call. Here the arrival pattern of the calls is assumed to follow Poisson process. The cell consists of N Channels. If free channels exist, both the calls will be served. If channels are not available, then the new calls will be dropped. Two types of Queues are assumed. The queue for handoff calls QHC and queue for originating

calls QOC respectively. If no channels are available the

handoff calls are queued in QHC, whose capacity is CHC

.The new calls are queued in QOC, only if the available

channels at the time of arrival are less than (N-R). The capacity CHC of QHC is large enough so that blocking

probability of the handoff call is neglected. If there is already N number of sessions in a cell, and a user wants a connection, due to non-availability of channels, user‟s request for the connection will be blocked or denied by the system. As a result a request for connection may be dropped i.e., a user has to Initiate a new request for connection next time, or may be queued i.e., the system is keeping the request in hold and waiting for a channel to be Free.

The Blocking Probability of the new calls when the calls are queued is stated as

Where

When handoff calls are queued and when no free channels are available, the probability of blocking is determined as:

The blocking probability for new call is:

Probability of call dropping is:

Where

3.3 Dynamic Reservation based on prediction

The Mobile Users tent to travel often. Since the MU has the freedom to move anywhere arbitrarily, the static resource reservation will not provide efficient usage of bandwidth. So by predicting the MU‟s next move the resource required for the MU may be reserved dynamically. Among various optimization methods, Genetic Algorithm is found suitable. The prediction was performed based on the past history of the mobile user. GA performs multidirectional search and produces a number of solutions in place of one solution. These solutions are called population. Each individual solution is called chromosome. Initial chromosomes are generated randomly. The fitness function is an important factor in GA because it not only defines the good solution but also reports the near optimal solution. The history of the mobile user is studied to obtain the history database.

Genetic Algorithm picks a closest match when there are many numbers of possible solutions to a problem. By properly defining the functions, implementation operations and area of GA one can expect an access to best solution. A set of potential gene created by GA are evaluated against some fitness criteria to access the required performance. If the criterion is met satisfactory the genes are interchanged and another set of solution is bred in a crossover. The algorithm starts by generating this population. The fitness of each individual in a generation is evaluated and modified to form a new population. The response of these accidental chromosomes is used to build a new population based on the stability so that a new reproduction is developed. The process is kept continuous for a fixed number of cycles or for a generated cutoff condition is met. Mutation is introduced at regular intervals to add some genes in order to avoid the stagnation of the algorithm. In this way the GA breeds solutions by creating „fitter‟ offspring and hence the analogy with natural selection: crossover creates new chromosomes from successful ones, and mutation ensures diversity.

International Journal of Emerging Technology and Advanced Engineering

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One important factor for call failures is network congestion. Multimedia calls such as video transfer, video conferencing are one of the important cause for network congestion. Since large number of connection requests may have to be handled by the network, QoS for such calls becomes a tedious. In order to achieve guaranteed QoS, there sources in there sources in the network must be allocated properly to ensure the call failures is minimum. Since it is very difficult to make the network capacity fit the demand during peak periods, alternative solutions have to be found to achieve a better utilization of this limited capacity. The congestion in the network may be avoided by cell splitting, frequency reuse etc., but the other dimension to control the congestion is by economic means. The number of incoming users to the cell could be controlled by impacting the pricing concept on the calls. One of these is to manage the incoming calls more efficiently, in order to guarantee a satisfactory level of quality of service (QoS). By implementing this concept it was evident that the network congestion was reduced and hence the blocking probability. This also improved the revenue of the network which is not our objective.

Where c0 is the normal price, c(t) is the cost charged to users at time t, which is the sum of normal price and extra peak hour price (if applicable). is the percentage of users that will accept this price.

The desired QoS is obtained if the cost is set as

3.5 Improving network revenue based inter frequency handover

Coverage and capacity is a main issue in the network Planning a network.

The proposed work supports different types of service providers and mobile users. The nodes have specific parameters like node id, position, velocityand power. Here the admission control is based on the availability of bandwidth. The handoff procedures may be horizontal or vertical based on the nature of the call and the mobile equipment. This approach of design a multi radio which considers the network cost. This attempt tries to make a compromise between service cost and best effort service.

.

IV. CONCLUSION

The research proposed here concludes with a new optimized algorithm to reduce the call failures and maximize the utilization of bandwidth. This analysis put forward the necessacity to manage the available scarce resources efficiently. The research carried out here integrated the concepts of Pricing, inter frequency handoff and CAC to maximize the usage of bandwidth. Three different CAC schemes are proposed here after carefully examining the characteristics of the network.

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