In a communication network, channel assignment is an essential component that can be divided into three categories which are static, dynamic, and hybrid channel allocation. In this paper, we focus on the dynamic channel allocation that is applied in Wireless Mesh Networks (WMN) where communication between two nodes occurs when they are both assigned the same channel. Wireless networking is a way to connect various equipment without having to install expensive cables throughout a place. Channel assignment in the networks is an application of graph theory on the vertex coloring. Three types of interference are considered in our work which are adjacent channel, cochannel and cosite constraints. The channels are allocated in such a way to minimize the interference and maximizing the performance of the networks. The interference is said to be minimized when all the constraints are considered, so that the interference can be avoided. The network is optimized if the interference reduced with a minimum number of the channel used. An algorithm called Improved Greedy Algorithm was proposed to solve the multi-channel assignments in wireless mesh networks by considering the adjacent channel, cochannel and cosite constraints. The Improved Greedy Algorithm has been tested with different values of constraints. From the results, it can be seen that the Improved Greedy Algorithm perform significantly well for the channel assignment problem.

1.
V.
Claudia
,
M.
Mirna
and
M.
Jezreel
,
presented at the 2013 International Conference on Mechatronics, Electronics and Automotive Engineering
,
2013
.
2.
Y.
Ding
,
K.
Pongaliur
and
L.
Xiao
,
IEEE Transactions on Mobile Computing
12
(
2
),
206
218
(
2011
).
3.
M.
Samaka
and
K. M.
Khan
,
Wiley Encyclopedia of Computer Science and Engineering
,
3089
3099
(
2007
).
4.
I. F.
Akyildiz
,
X.
Wang
and
W.
Wang
,
Computer networks
47
(
4
),
445
487
(
2005
).
5.
L.
Yang
,
Y.
Li
,
S.
Wang
and
H.
Xiao
,
IEEE Access
7
,
67167
67177
(
2019
).
6.
Y.
Bu
,
S.
Finbow
,
D. D.-F.
Liu
and
X.
Zhu
,
Discrete Applied Mathematics
167
,
45
51
(
2014
).
7.
S.
Salleh
and
N. A.
Salahudin
,
presented at the Proceedings of the International Conference on Modeling, Simulation and Visualization Methods (MSV)
,
2011
.
8.
Y.
Ding
and
L.
Xiao
,
Computer Communications
34
(
7
),
803
815
(
2011
).
9.
D.
Yang
,
X.
Fang
,
N.
Li
and
G.
Xue
,
presented at the GLOBECOM 2009-2009 IEEE Global Telecommunications Conference
,
2009
.
10.
A. E.
Araqi
and
B.
Mahboobi
,
presented at the 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)
,
2019
.
11.
T.
Matsui
,
N.
Sukegawa
and
A.
Miyauchi
,
Information Processing Letters
114
(
12
),
706
709
(
2014
).
12.
J.
Bang-Jensen
,
G.
Gutin
and
A.
Yeo
,
Discrete optimization
1
(
2
),
121
127
(
2004
).
13.
S.
Salleh
,
A. Y.
Zomaya
,
S.
Olariu
and
B.
Sanugi
,
Numerical simulations and case studies using Visual C++. Net
. (
John Wiley & Sons
,
2005
).
14.
E.
Malaguti
and
P.
Toth
,
International transactions in operational research
17
(
1
),
1
34
(
2010
).
15.
A.
Raniwala
,
K.
Gopalan
and
T.-c.
Chiueh
,
ACM SIGMOBILE Mobile Computing and Communications Review
8
(
2
),
50
65
(
2004
).
16.
S. H.
Lim
and
Y. B.
Ko
,
presented at the 2020 IEEE International Conference on Big Data and Smart Computing (BigComp)
,
2020
.
17.
B. M.
Ismael
,
A. B.
Ngadi
and
J. B. M.
Sharif
,
presented at the 2021 International Conference on Data Science and Its Applications (ICoDSA)
,
2021
.
18.
M.
Portmann
and
A. A.
Pirzada
,
IEEE Internet computing
12
(
1
),
18
25
(
2008
).
19.
F. S.
Roberts
,
Discrete mathematics
93
(
2-3
),
229
245
(
1991
).
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