EFFECT OF BLOCKING PROBABILITY ON CHANNEL ALLOCATION

USING DISTRIBUTED DYNAMIC CHANNEL ALLOCATION ALGORITHM

1

Y.S.V. RAMAN,

2

S. SRI GOWRI &

3

B. 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 D

min

[1], otherwise cannot use the same channel due to interference A cell

C

k

is said to be an interference neighbors of another cell, C

m

, if geographical distance between them is

less than minimum reuse distance D

min

. 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

International Journal of Computer Networking,

Wireless and Mobile Communications (IJCNWMC)

ISSN 2250-1568

Vol.2, Issue 3 Sep 2012 27-35

© TJPRC Pvt. Ltd.,

28 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 probabil ity 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 29

Fig1: A Cellular Network with Mobile Base Stations 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.=

2

212121

2

21

)())(()( jjjjiiii ++

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 t he 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

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

borrowers request for some channel r, it marks thi s channelfor transfer. It will not use this channel since

the channel has been marked for transfer. In addition, it will not grant any other borrowers request for the

same channel.

PROPOSED ALGORITHM

Fig 3: Cellular System with Cluster Size of Three

N

k

, NP

k

(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 C

k

is represented by N

k

.

N

k

= {C

m

⎜distance (C

k

,C

m

) < D

min

}. Where cell C

m

is the member of N

k

.

B

k

: C

k

can borrow a set of channels

Sk: set of channels that Ck attaches with its reply.

Pc: set of primary channels assigned to C

k

.

Uc: set of used channels of C

k

. Initially Uc is empty.

I

k

(r): the set of cells to which C

k

has sent an agree(r) message. If I

k

(r) ≠ φ, r is an interference

channel of C

k

then C

k

cannot use r, but it can lend it to other cells. Initially I

k

(r) is empty.

Ci

k

(k,m): A set of cells, which saves the state I

k

(r), when C

k

receives C

y

s request message.

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 G

k

= P

k

-U

k

-{r / r∈ P

k

∈ I

k

(r) ≠ φ} is

calculated, if G

k

is empty, C

k

sets a timer and sends a request message to every c ell

C

m

∈N

k

;otherwise, it picks a channel r∈ G

k

and adds r to U

k

.When the call is completed, it

deletes r from U

k

.

2. When a cell C

k

receives a request from C

m

, it sends reply (P

k

-U

k

-{r⎪r∈ P

k

, N

k

(r)∈N

m

≠φ} to

C

m

,for any r∈P

k

, C

k

(r,y) ∈ I

k

(r).

3. When a cell C

k

receives all reply (S

m

) messages or timeout, it sets a new time and does

following.

a. For each r in the system, B

K

=B

k

∈ r if C

k

is not using r For any C

Z

∈ NP

k

(r), C

k

has received a

reply (S

z

) from C

z

, and For any C

m

∈NP

k

(r), r∈S

b. If B

k

is not empty; C

k

selects a channel r∈B

k

using underlying channel selection strategy and

sends confirm(r) message to cells in NP

K

(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 probabil ity 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.

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

blocking probability

spectral efficiency

spectral efficiency Vs Blocking probability

400MHz

500MHz

600MHz

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

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

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

1

1.5

2

2.5

3

blocking probability

spectral efficiency

Spectral efficiency Vs Blocking Probability

400MHz

500MHz

600MHz

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

0.05

0.1

0.15

0.2

0.25

1

1.5

2

2.5

3

blocking probability

spectral efficiency

Spectral efficiency Vs Blocking probability

400MHz

500MHz

600MHz

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.05

0.1

0.15

0.2

0.25

0.3

0.35

blocking probability

Throughput

Throughput Vs Blocking Probability

400MHz

500MHz

600MHz

Fig6:Spectral Efficiency vs Blocking probability Fig7:Throughput vs Blocking Probability

For(N=32) For (N= 8)

Effect of Blocking Probability on Channel Allocation Using Distributed Dynamic Channel Allocation Algorithm 33

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Blocking probability

Throughput

400MHz

500MHz

600MHz

0.1

0.2

0.3

0.4

0.5

0.6

0.2

0.4

0.6

0.8

1

1.2

1.4

Blocking probability

Throughput

Blocking probability Vs Throughput

400MHz

500MHz

600MHz

Fig8:Throughputvs Blocking Probability 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.

REFERENCES

1. Jianping JiangJiangping et.al.On distributed dynamic channel allocation in mobile cellular

networks.IEEE transactions on parallel and distributed system,13(10):1024-1037,2002.

2. [2] Scott Jordan. Resourse allocation in wireless networks. Journal of high speed networks,

5(1):23-34, 1996.

3. 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.

4. Katzela et.al. Channel assignment schemes for cel lular mobile teleco-cmmunication

systems.IEEE personal communication 10-31,1996.

5. Jianchang Yang et.al. A fault- tolerant channel allocation algorithm for cellular network with

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6. Guhong Cao et.al. An adaptive distributed channel allocation strategy for mobile cellular

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

7. Guhong Intergrating distributed channel allocation and adaptive handoff management for QoS

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34 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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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): 1874-

1888,November 1999.

11. W.Yue,Analytical methods to calculate the performa nce 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 me thods 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

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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 Communicatio ns 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.

Effect of Blocking Probability on Channel Allocation Using Distributed Dynamic Channel Allocation Algorithm 35

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 PhDs 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

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