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 setup 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 22501568
Vol.2, Issue 3 Sep 2012 2735
© 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 cochannel 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 cochannel 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 systemwide
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 cochannel
interference. Distributed dynamic channel allocation scheme proposed by has used a resourceplanning
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 threecell cluster model using distributed dynamic channel allocation algorithm
analysis [9],[10]. Conventional cellular system can use threecell 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 cochannel cells allocated with
channel set B, and cells 4,5 and 6 are cochannel 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 commonfree
channels. Randomly select a channel from a disjoint subset of cells, which has maximum
number of commonfree channels.
5. If two disjoint subset of cells having same number of commonfree 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.
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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 FaultTolerant
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communication system with hybrid channel assignment, IEEE Trans.
Vehicular Technology Conf.,1991,pp.549553.
12. P.O.Gaasvik,M.Cornefjord& V.Svensson Different me thods of giving priority to handoff
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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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