3.Comp Networking - IJCNWMC - Effect of - YSV Raman- Paid

International Journal of Computer Networking, Wireless and Mobile Communications (IJCNWMC) ISSN 2250-1568 Vol.2, Issue 3 Sep 2012 27-35 Š TJPRC Pvt. Ltd.,

EFFECT OF BLOCKING PROBABILITY ON CHANNEL ALLOCATION USING DISTRIBUTED DYNAMIC CHANNEL ALLOCATION ALGORITHM 1

Y.S.V. RAMAN, 2S. SRI GOWRI & 3B. PRABHAKARA RAO 1

Associate Prof, ECE Dept, KL University, Guntur Dist., India

2

Prof and Hod ECE Dept, S.R.K.Institute of Technology, Enikepadu, Vijayawada, India 3

Professor ECE Dept, JNTU Kakinada, India

ABSTRACT Technological advances and rapid development of handheld wireless terminals have facilitated the rapid growth of wireless communications and mobile computing. Essentially we have a limited resource transmission spectrum, which must be shared by several users. Since the available frequency spectrum is limited the channels must be reused as much as possible in order to increase the system capacity. This requires a proper channel allocation scheme. The role of a channel allocation scheme is to allocate channels to cells or mobiles in such a way as to minimize call blocking or call dropping probabilities. The process of channel allocation must satisfy the electromagnetic compatibility constraints known as hard constraints and the demand of channels in a cell for new calls as well as existing calls. In this paper, we propose a distributed dynamic channel allocation (DDCA) algorithm show blocking probability of distributed dynamic channel allocation is reduced by increase spectral efficiency. The proposed algorithm is based on a distributed dynamic channel allocation technique is to increase the throughput of the system for an blocking probability.

KEYWORDS: Distributed Dynamic Channel Allocation, Blocking Probability, Throughput INTRODUCTION In cellular communication there are two types of channels are available between MH and MSS: communication channel and control channel. Establishing a communication session between MSS and MS in a cell, communication channel is used while control channel is the set-up channel used to send messages. In cellular system two cells can use the same channel if the distance between these cells have the minimum reuse distance Dmin [1], otherwise cannot use the same channel due to interference A cell Ck is said to be an interference neighbors of another cell, Cm, if geographical distance between them is less than minimum reuse distance Dmin. When mobile host needs a channel to support a call it sends request message to MSS in its cell, the MSS tries to assign a channel to the mobile host (MH) using channel allocation scheme as shown in Fig 1.The channel allocation schemes can be classified in three categories, Fixed channel allocation (FCA), Dynamic channel allocation (DCA) and Hybrid channel allocation (HCA).In fixed channel allocation [2] is typically used by first generation macrocellular

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Y.S.V. Raman, S. Sri Gowri & B. Prabhakara Rao

systems where disjoint subsets of the available channels are permanently allocated to the cells in advance according to their estimated traffic loads. The cells are arranged in tessellating reuse clusters whose size is determined by the co-channel reuse constraint. In dynamic channel allocation [3] is one well known solution to the microcellular channel assignment problem, where the dynamic nature of the strategy permits adaptation to spatial and temporal traffic variations while the distribution of control reduces the required computation and communication among base stations (BSs), thereby reducing system latencies.

DCA schemes have no exclusive relationship between cells and channels, and in their most general form they allow any cell to use any channel that does not violate the co-channel reuse constraint. DCA schemes are known to outperform FCA under conditions of light nonstationary traffic. In hybrid channel allocation [4], few channels are permanently allocated to each cell and the remaining channels are allocated dynamically. The performance of the hybrid channel allocation schemes are intermediate between fixed and dynamic channel allocation schemes. The dynamic channel allocation schemes are divided into two types centralized and distributed. [5] Centralized DCA schemes require system-wide information and control for making channel assignments. In Distributed dynamic channel allocation [6], there is no central controller such as MSC. The MSSs share the responsibility to allocate channels. Each MSS makes decision independently based on its local information. They exchange information if necessary, in order to compute the set of available channels such that using them causes no co-channel interference. Distributed dynamic channel allocation scheme proposed by has used a resource-planning model, uses cluster size 9 in which the set of cells in the system model is partitioned into 9 disjoint subsets. Every cell in a disjoint subset is in the minimum reuse distance. The numbers of channel are also divided into 9 disjoint sets of channels. The each partitioned group assigned a channel group. DDCA uses the cluster size 9, which contains 30 interfering neighbors. When a channel needs by the cell has to send the request message to all its interference neighbors, thus the message complexity of the algorithm is high. This high message complexity of algorithm needs to develop new system model, which reduces message complexity to some extent. The proposed system model of size 6*6 with cluster size 3 is considered, means the set of cells are partitioned into 3 disjoint sets and number of channel sets is also divided into 3 channel disjoint subsets. In the proposed system model when a channel needs to be borrowed by a cell, it has to send request message to its 6 interfering neighbors in spite of 30 interfering neighbors as proposed in [7]. The blocking probability and message complexity of the proposed algorithm is reduced; hence the performance of cellular system increases.

Effect of Blocking Probability on Channel Allocation Using Distributed Dynamic Channel Allocation Algorithm

Fig1: A Cellular Network with Mobile Base Stations

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Fig2: A 49 Hexgonal Cell System

SYSTEM MODEL The mobile cellular network is regular grid of hexagonal cells of radius (R). The cells are organized as (N xN) array as shown in Fig. 2. An array of (N x N) cellular network has (i) rows and (j) columns of cells. The cell at (i) row and (j) columns denoted by cells(i, j). Then the Euclidean distance between two cell centers is given by El.dis.= (i1 −i2 ) +(i1 −i2 )(j1 − j2 ) +( j1 − j2 ) 2

2

Each cell contains base station that communicates with mobiles in cell by transmitting and receiving signals on radio links. The transmission from base station to mobile referred as forward link. The corresponding transmission from a mobile to base station is reverse link. When mobile host wants to set up a call, it sends a request on its control channel to its base station in its cell. The call can be set up only if channel is assigned to support the communication between the mobile host and the base station.

RELATED WORK Let us assume a three-cell cluster model using distributed dynamic channel allocation algorithm analysis [9],[10]. Conventional cellular system can use three-cell reuse pattern as shown in Fig(3).Consider a typical cell, say cell 0 as shown in Fig 3. Let cell 0 be allocated with nominal channel set A and let its six neighbors be labelled such that cells 1,2 and 3 are co-channel cells allocated with channel set B, and cells 4,5 and 6 are co-channel cells allocated with channel set C. We call cells 1, 2 and 3 Group B cells, and cells 4, 5 and 6 Group C cells. When a cell needs a channel to support a call, it first checks whether there exists an available channel allocated to it. If such a channel exists, then it picks that channel to support the call. Otherwise, it sends a request message to each of its interference neighbors. Based on the information received from the neighbors, it computes the set of available channels that can be borrowed. It picks one such channel and consults the lenders whether it can use this channel. If all lenders grant its request, then it uses this channel to support the call. After a lender grants a

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Y.S.V. Raman, S. Sri Gowri & B. Prabhakara Rao

borrower’s request for some channel r, it marks this channelfor transfer. It will not use this channel since the channel has been marked for transfer. In addition, it will not grant any other borrower’s request for the same channel.

PROPOSED ALGORITHM

Fig 3: Cellular System with Cluster Size of Three Nk, NPk (r) is the interference neighbors and interference partition subset of Ck relative to channel k, respectively. Let a cell Ck, the set of interference neighboring cells of Ck is represented by Nk. Nk = {Cm⎜distance (Ck,Cm) < Dmin}. Where cell Cm is the member of Nk. Bk : Ck can borrow a set of channels Sk: set of channels that Ck attaches with its reply.

Pc: set of primary channels assigned to Ck. Uc: set of used channels of Ck. Initially Uc is empty.

Ik(r): the set of cells to which Ck has sent an agree(r) message. If Ik(r) ≠ φ, r is an interference channel of Ckthen Ck cannot use r, but it can lend it to other cells. Initially Ik(r) is empty.

Cik (k,m): A set of cells, which saves the state Ik(r), when Ck receives Cy’s request message.

(1) The Offered load ‘G’is given by[12],[13]

Where G is offered traffic per cell, n is the number of channels per cell, B is the Bandwidth per channel(MHz), β is The number of users, b is the blocking probability.

Effect of Blocking Probability on Channel Allocation Using Distributed Dynamic Channel Allocation Algorithm

31

Phase 1 1.

When a cell needs a channel to support a call request, let Gk = Pk-Uk-{r / r∈ Pk ∈ Ik(r) ≠ φ} is calculated, if Gk is empty, Ck sets a timer and sends a request message to every cell Cm∈Nk;otherwise, it picks a channel r∈ Gk and adds r to Uk.When the call is completed, it deletes r from Uk.

2.

When a cell Ck receives a request from Cm, it sends reply (Pk-Uk-{r⎪r∈ Pk, Nk(r)∈Nm≠φ} to Cm,for any r∈Pk, Ck(r,y) ∈ Ik(r).

3.

When a cell Ck receives all reply (Sm) messages or timeout, it sets a new time and does following.

a.

For each r in the system, BK=Bk ∈ r if Ck is not using r For any CZ∈ NPk(r), Ck has received a reply (Sz) from Cz, and For any Cm ∈NPk(r), r∈S

b.

If Bk is not empty; Ck selects a channel r∈Bk using underlying channel selection strategy and sends confirm(r) message to cells in NPK(r); otherwise call will be dropped.

Phase 2 4.

In the entire replies find the common number of free channel/channels available in each disjoint subset of cells, determines which disjoint subset of cells has maximum number of common-free channels. Randomly select a channel from a disjoint subset of cells, which has maximum number of common-free channels.

5.

If two disjoint subset of cells having same number of common-free channels replied, then that disjoint subset of cells is selected which reply earliest to the requesting cell and it acts as a lender.

RESULTS Considering various number of channels and area of the cell equal to 10 sq.m.using different bandwidths the throughput is calculated for the number of users are 4096. The plots Fig(4),Fig(5),Fig(6) are drawn for spectral efficiency blocking probability by varying bandwidth of the channel. The performance of algorithm is tested also by obtaining the relation between throughput versus blocking probability Fig(7),Fig(8),Fig(9) for different bandwidths of the channel. spectral efficiency Vs Blocking probability 7

400MHz 500MHz 600MHz

6.5 6

spectral efficiency

5.5 5 4.5 4 3.5 3 2.5 2 0.15

0.2

0.25

0.3 0.35 0.4 blocking probability

0.45

0.5

0.55

0.6

Fig4: Spectral Efficiency vs Blocking Probability for (N=8)

32

Y.S.V. Raman, S. Sri Gowri & B. Prabhakara Rao

Spectral efficiency Vs Blocking Probability 400MHz 500MHz 600MHz

s pec tral effic ienc y

3

2.5

2

1.5

1 0.04

0.06 0.08

0.1 0.12 0.14 0.16 0.18 blocking probability

0.2

0.22

Fig5: Spectral Efficieny vs Blocking Probability Probability for (N=16)

Spectral efficiency Vs Blocking probability 400MHz 500MHz 600MHz

3

Throughput Vs Blocking Probability 400MHz 500MHz 600MHz

0.35

Throughput

s pec tral effic iency

0.3

2.5

2

0.25

0.2

0.15

1.5 0.1

1 0.05

0.05

0.1

0.15 blocking probability

0.2

0.25

Fig6:Spectral Efficiency vs Blocking probability For(N=32)

0.05

0.1

0.15

0.2 0.25 0.3 0.35 blocking probability

0.4

0.45

0.5

0.55

Fig7:Throughput vs Blocking Probability For (N=8)

33

Effect of Blocking Probability on Channel Allocation Using Distributed Dynamic Channel Allocation Algorithm

Blocking probability Vs Throughput 400MHz 500MHz 600MHz

1.4

0.8 400MHz 500MHz 600MHz

0.7

1.2

Throughput

0.6

T hroughput

0.5 0.4

1

0.8

0.6

0.3 0.2

0.4

0.1

0.2 0

0

0.1

0.2

0.3 0.4 Blocking probability

0.5

0.6

0.1

0.7

Fig8:Throughputvs Blocking Probability

0.2

0.3 0.4 Blocking probability

0.5

0.6

Fig 9:Throughput vs Blocking Probability

for(N=16)

for(N=32)

CONCLUSIONS In this paper, we implemented distributed dynamic channel allocation technique to utilize the multiple channels available

in cells. It is observed from the results that the blocking probability

decreased with increase in spectral efficiency.When the throughput is increasing blocking probability also decreased. It has also been observed that throughput increased in decreasing the

blocking

probability as well bandwidth of the channel.

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Jianping JiangJiangping et.al.On

distributed dynamic channel allocation in mobile cellular

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[2] Scott Jordan. Resourse allocation in

wireless networks. Journal of high speed networks,

5(1):23-34, 1996.

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Justin C.I. et.al. Performances

issues and algorithms for dynamic channel assignment.IEEE

journal on selected areas in communications 11(6):955- 963, 1993.

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Katzela et.al. Channel assignment schemes

for

cellular

mobile teleco-cmmunication

systems.IEEE personal communication 10-31,1996.

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Jianchang Yang et.al. A fault- tolerant channel allocation algorithm for cellular network with mobile base stations.IEEE transactions on vehicular technology 56(1):349-361, 2007.

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Guhong Cao et.al. An adaptive distributed channel allocation

strategy for mobile cellular

networks.Journal of parallel and distributed computing, 60:451-473,2000.

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Guhong Intergrating distributed channel allocation and adaptive handoff management for QoS sensitive cellular networks, (9): 131-142,2003.

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Y.S.V. Raman, S. Sri Gowri & B. Prabhakara Rao

8.

Jianchang Yang, Qiangfeng Jiang, D. Manivannan, and Mukesh Singhal,” A Fault-Tolerant Distributed Channel Allocation Scheme for Cellular Networks” IEEE Transactions on Computers,vol. 54,no. 5,pp 616-629,May 2005

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Jianchang Yang and D. Manivannan, An Efficient Fault-Tolerant Distributed Channel Allocation Algorithm for Cellular Networks, IEEE Transactions on Mobile computing 4(6): 578-587,November 2005.

10. Ravi Prakash,Niranjan G.,Mukesh Singhal. Distributed Dynamic Fault Tolerant channel allocation for cellular networks,IEEE Transactions on Vehicular Technology,vol48(6): 18741888,November 1999.

11. W.Yue,”Analytical methods to calculate the performance of a cellular mobile radio communication

system

with

hybrid

channel

assignment,”

IEEE

Trans.

Vehicular Technology Conf.,1991,pp.549-553.

12. P.O.Gaasvik,”M.Cornefjord& V.Svensson “Different methods of giving priority to handoff traffic in a mobile telephone system with directed retry,” in Proc.41st Veh.Technology Conf.,1991,pp.549-553.

13. Raqibul Mostafa, Annamalai Annamalai,Jeffrey H.Reed,”Performance evaluation of Cellular mobile radio systems with interference nulling of dominant interferers”,IEEE Transactions of Communications,vol.52,no.2,pp;326-335,February 2004.

Y.S.V.Raman completed his M.Tech in Radar and microwave

engineering from Andhra University,

Visakhapatnam. He pursuing Phd in JNTU Kakinada , and a member of ISTE. Presently working as Associate

Professor

in

Electronics

and

Communications

Engineering

dept,

K.L.University,

Vaddeswaram, Guntur District, Andhrapradesh. He has 12 years of experience in teaching and 3 years of experience in industry. He published 3 papers in National/International Journals.

35

Effect of Blocking Probability on Channel Allocation Using Distributed Dynamic Channel Allocation Algorithm

Dr.S.Sri Gowri has more than 17 years of experience in teaching.She is an

expert in Digital

communications.She is guiding 7 PhD scholars.Presently she is working as Prof &Head of the ECE Department , S.R.K. Institute of Technology,Enikepadu,Vijayawada.She is a member of ISTE and IETE. She published around 50 papers in various National/International Journals and conferences.

Her areas

of Interests are Mobile Communication, Mobile Networking

4G

Technologies.

Dr.B.Prabhakara Rao has more than 28 years of experience in teaching and 20 years of R & D. He is an expert in Signal Processing & Communications .He produced 5 PhDâ&#x20AC;&#x2122;s and guiding 25 PhD scholars.

He held

Head of the Department, in JNTU College of Engineering. Presently working as Director of

Evaluation in JNTU Kakinada. He published more than 85 technical papers in national and International journals and conferences