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One of the main challenges of multi-radio multi-channel wireless mesh networks (MR-MC WMNs) is how to efficiently avail from the radio resources available in the network. The level of interference experienced by the mesh nodes rises when the network becomes more connected and the number of radios per node increases. To mitigate interference experienced by the wireless mesh nodes while taking advantage from the multi-channel network aspect, we propose a novel Interference-aware Game based Channel Assignment algorithm, named IGCA. We prove through simu-lations that our proposed algorithm contributes in alleviating the node interference degree, fairly allocates interfaces to net-work non-overlapping channels and increases simultaneous transmissions in the network. An improvement up to 50% of both interference degree and simultaneous connections is particularly observed in comparison with a prominent existing approach -the Near-optimal Partially Overlapping Channel Assignment algorithm.
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Interference-aware Game-based Channel
Assignment Algorithm for MR-MC WMNs
Amira BEZZINA, Mouna AYARI, Rami LANGAR, Guy PUJOLLE, Leila SAIDANE
LIP6 Laboratory, University of Paris VI, Paris, France
CRISTAL lab, National School of Computer Sciences, University of Manouba, Tunisia
Emails: {amira.bezzina, rami.langar, guy.pujolle}@lip6.fr, mouna.ayari@cristal.rnu.tn, leila.saidane@ensi.rnu.tn
Abstract—One of the main challenges of multi-radio multi-
channel wireless mesh networks (MR-MC WMNs) is how
to efficiently avail from the radio resources available in
the network. The level of interference experienced by the
mesh nodes rises when the network becomes more connected
and the number of radios per node increases. To mitigate
interference experienced by the wireless mesh nodes while
taking advantage from the multi-channel network aspect,
we propose a novel Interference-aware Game based Channel
Assignment algorithm, named IGCA. We prove through simu-
lations that our proposed algorithm contributes in alleviating
the node interference degree, fairly allocates interfaces to net-
work non-overlapping channels and increases simultaneous
transmissions in the network. An improvement up to 50%
of both interference degree and simultaneous connections is
particularly observed in comparison with a prominent existing
approach - the Near-optimal Partially Overlapping Channel
Assignment algorithm.
Keywords: Wireless Mesh Networks, Multi-Radio Multi-
Channel, Channel Assignment, Interference Aware, Game
Theory, Potential Game.
I. INTRODUCTION
Future wireless mobile communications will be driven
by high-converge networks that integrate a wide range
of technologies and services. In this perspective, wireless
mesh networks (WMNs) are considered as a potentially
attractive key-solution to provide broadband wireless ac-
cess services. Due to their promising features including
self-organization, self-configuration, easy network main-
tenance and reliable service coverage, such networks pro-
vide a flexible high-bandwidth wireless backhaul over
large geographical areas, especially when multiple chan-
nels and multiple radio interfaces are deployed.
While single radio mesh nodes operating on a single
channel suffer from capacity constraints, deploying mul-
tiple radios and multiple channels on mesh routers can
improve significantly the capacity of the network and
increase the aggregate bandwidth [1] [2]. However, con-
sidering an efficient channel assignment which pursues an
appropriate mapping between the available channels and
nodes’ radios is required. In fact, despite the availability of
multiple frequencies offered by the IEEE 802.11 standards,
the total number of radio interfaces in a WMN is much
higher than the number of available channels. So, many
neighboring nodes radio interfaces could be assigned the
same channel or channels that are partially overlapping
to each other resulting in a network performance degra-
dation due to interference problem.
To design an efficient channel assignment in multi-radio
multi-channel WMNs, many important issues should be
handled carefully such as minimizing interference effect,
maintaining the network connectivity and improving the
aggregate network capacity. To reach this goal, channel
assignment for multi-channel wireless mesh networks has
been widely proposed in the literature, but still very
knotty when it comes to meet all the challenges.
In this paper, we investigate how to design an effi-
cient channel assignment algorithm that reaches these
requirements. Specifically, we propose a new interference-
aware game-theoretic approach for channel assignment
in a mesh backbone. We first formulate the problem as
a potential game, i.e, an identical interest game. Indeed,
mesh routers (or MRs) are modeled as players trying
to maximize a specific utility in order to alleviate the
"a priori" interference between them. We then describe
our proposed Interference-aware Game-based Channel
Assignment Algorithm (IGCA) with perfect information.
To gauge the effectiveness of our proposal, we compare
the IGCA algorithm with a prominent existing approach:
the Near-optimal Partially Overlapping Channel Assign-
ment algorithm [3]. Results show that IGCA reduces
significantly nodes interference degree, ensures a fair
distribution of radios between channels and most im-
portantly permits a considerable number of simultaneous
connections in the mesh backbone, when compared to
NPOCA.
The reminder of the paper is organized as follows.
Section II discusses literature that is relevant to this work.
Section III presents the system model used in our ap-
proach and the problem formulation as a potential game.
The game-based proposed algorithm is then introduced
in section IV. Simulation results are provided in section
V. Finally, section VI concludes this paper.
II. RELATED WORK
Owing to the importance of efficiently assigning ra-
dios to channels in order to improve the performance
of MR–MC WMNs, extensive studies have been carried
out to tackle this issue. Several multi-channel allocation
solutions have been proposed in the literature. Each one
of them focuses on a specific aspect and addresses a
particular need. Comprehensive literature surveys pro-
viding interested classifications of channel assignment
approaches designed for MR-MC WMNs can be found in
[2] [4] [5]. Here, we mention only studies that are directly
relevant to our work, i.e. those that have applied game
theory to solve the channel assignment (CA) problem [6].
Gao et al. presented in [7] a static cooperative game
with perfect information in which players within a coali-
tion collaborate to achieve high data rates. The focus was
on the performance improvement of the multihop links,
induced by cooperation gains, without sacrificing the
performance of single-hop ones. Authors introduced the
min-max coalition-proof Nash equilibrium (MMCPNE)
channel allocation scheme in the game. However, this
work has mostly a theoretical interest. Some assumptions
made by the authors like the fact that "each node partic-
ipates in only one communication session" and that "the
whole network is a single collision domain" do not reflect
usually a real network behavior.
In [8] and [9], the CA problem has been formulated as a
non-cooperative game where each node aims at maximiz-
ing its own profit selfishly. Yang et al. proposed in [8] a
CA scheme designed for MR-MC wireless networks with
multiple collision domains. The proposed strategic game
named ChAlloc has been formulated as a strategic game.
To avoid possible oscillation in ChAlloc game, a charging
scheme was designed to induce players to converge to a
Nash Equilibrium. Manikantan Shila et al. [9] proposed an
algorithm achieving a load balancing Nash Equilibrium
solution in a selfish and a topology-blind environment.
The algorithm is based on imperfect information for single
collision clique wireless networks. The solution operates
in three stages, each stage focuses on improving the total
achievable data rate of each node.
The two above proposed schemes are very interesting.
However, they do not match well with our specific context
of application. They are designed for non cooperative
MC-MR wireless networks, where the network consists
of heterogeneous wireless nodes each owned by an in-
dependent individual. Nevertheless, we consider in our
study wireless mesh backbones where mesh routers tend
to be cooperative since they are managed by the same
administrator. Assuming that mesh backbone nodes may
have a selfish behavior neglecting the system performance
may not hold in practice.
Nezhad et al. proposed SICA [10], a game formulation
of CA taking into account the co-channel interference. The
proposed interference-aware CA scheme is semi-dynamic
and distributed. Besides, it applies an on-line learner
algorithm which assumes that nodes do not have perfect
information. Thus, players (mesh routers) play a mixed
strategy based on their weights to solve it. However,
besides having selfish nodes that try to occupy the best
channels, SICA uses three radios for each node (the first
to receive, the second to transmit and the third tuned to a
common channel for all nodes) which cannot be managed
in all WMNs.
In the same context and aiming to especially reduce
physical-layer interference, Yen et al. proposed in [11] a
two-stage radio allocation scheme where wireless inter-
faces are modeled as players participating in a radio re-
source game. On one hand, the first stage assigns channels
to radios using a game-theoretic approach. On the other
hand, the second stage assigns the resulting radio-channel
pairs to links using a greedy method.
Availing from partially overlapped Channels, Duarte et
al. presented respectively in [3] and [12] the Near-optimal
Partially Overlapping Channel Assignment (NPOCA)
and Heuristic Partially Overlapped Channel Assignment
(HPOCA) schemes using a cooperative and potential
game. These algorithms have the overall objective of
maximizing the network throughput while reducing co-
channel interference. Unfortunately, the proposed channel
selection mechanisms are not optimal. They broadcast a
lot of coordination messages in the network. Moreover,
they do not take into account the connectivity issue.
Later, we compare our proposal to the NPOCA scheme,
arising the interest in developing strategic approaches
with perfect information to solve the CA problem.
III. CONTEXT AND PROBLEM FORMULATION
We consider a wireless MR-MC backbone mesh con-
sisting of several mesh routers. In our study, we focus on
providing a suitable CA algorithm that aims to reduce
channel interference while keeping a connected network
and avoiding channel congestion. In the following, we
first present the necessary notions used in our model.
Then we expose the corresponding problem formulation
based on a game theoretic approach.
A. Notations
A={a1, a2, ..., an}is the set of nodes, where |A|
is the total number of nodes deployed in the mesh
backbone.
Idenotes the number of radios per node.
Cdenotes the number of non-overlapping channels
in the network.
kij is the number of radios of player iassigned to
channel j.
nij is the number of interfering neighbors of player
iwhich are using channel jon one of their radios.
Nidenotes the number of potentially interfering
neighbors of i(i.e. nodes in the interference range
of node i)
Sirepresents the strategy of player iand is denoted
by Si={ki1, ki2, .., kiC }
S=×iASi=S1×S2×... ×Snis the game profile
defined as the Cartesian product of the players’
strategy vector.
For our model, we assume the following:
PjCKij = 1 : All radios must be affected to
channels.
kij 1: Radios of a same player must be affected
to different channels.
• |C|> I : The number of interfaces per node is
smaller than the number of channels available in
the network.
Iis fixed and is the same for all players.
B. Utility
The main objective of our game is to minimize the
network interference. Thus, we define an interference-
aware metric Gias the gain of player i:
Gi= 1 X
jC
kij ×nij
Ni×I(1)
Each player is a decision maker which chooses a strat-
egy Si. It maximizes its gain by minimizing the cost of
interference expressed by the second term of the metric
Gi. This cost can be seen as a penalty fee imposed on
player i due to its choice.
The player’s utility is defined as:
Ui(S) = U(S) = PiAGi
|A|(2)
Theorem: The proposed channel allocation game is an
identical interest game.
Proof:
We first prove that our game is a potential game.
Then, we prove that it belongs to the specific subclass
of identical interest game.
A potential game is a normal form game such that
any change in the utility function of any player due to
a unilateral deviation by that player is correspondingly
reflected in a global function referred to as the potential
function [13].
Definition: A game is an exact potential game if there
is a function φ:SRsuch that SiS,S0
i, S00
iS:
φ(S0
i, Si)φ(S00
i, Si) = U(S0
i, Si)U(S00
i, Si)
Or, it is obvious that the utility function defined in
Eq. (2) is a potential function for IGCA. Thus, we deal
here with an exact potential game.
Besides, we have: φ(S) = Ui(S)=(S),iA.
As a result, our game is an identical interest game
(called also common interest game or team game). In such
game, the players’ utilities are chosen to be the same and
players aim to maximize their common utility. Identical
interest games are a particular case of exact potential
games [14]. Thus, they inherit all of their properties. In
fact, for a potential game, the following holds:
Every finite potential game possesses at least one
pure strategy Nash Equilibrium (i.e. deterministic
NE).
All NE are either local or global maximizers of the
utility function.
C. Algorithm
Given the utility function previously described in sec-
tion III.B, we propose a game-based algorithm with per-
fect information in which each player knows the strategies
of the others. We use in our model an "extensive form"
game where players play one after the other. Players’
choices are based on the "better response", a known
scheme used to reach utility function’s maximizers.
Obviously, the "better response" provides less intensive
computation at the cost of a slower convergence to the
equilibrium than the "best response". The latter provides
a fast convergence but requires intensive processing that
grows exponentially according to the size of the network.
Note that usually a wireless mesh backbone is managed
by an administrator. In order to reduce transmission over-
head, our algorithm uses a partially centralized approach
which can be suitable to a large scale mesh backbone. Be-
sides, it avoids congestion by flooding the network with
redundant information. Hence, we made the following
assumptions:
A common channel for communication between
players in the negotiation phase is assumed to be
available.
The real allocation of channels is done after the
execution of the following algorithm.
T is the stop condition in terms of time or maximum
number of negotiations.
Algorithm 1: IGCA Algorithm
Input: A, I, C
Output: S set of strategies S1, S2, . . . , S|A|
1Initiator SelectAnI nitiator(A)
2Order SelectRandomOrder(I nitiator, |A|)
3Broadcast(Order, A)
4for ifrom 1to |A|do
5Si(0) RandomV alidStrategy(I, C )
6Strat[i]SendI nitialStrategy(Initiator, Si(0))
7j1
8iOrder[j]
9sender Initiator
10 while T=false do
11 SendU pdatedS trategies(sender, i, Strat)
12 Sirand RandomV alidStrategy(I, C)
13 if Sirand > Si(t)then
14 Si(t+ 1) Sirand
15 else
16 Si(t+ 1) Si(t)
17 Strat[i]Si(t+ 1)
18 sender i
19 Updatej
20 iOrder[j]
21 UpdateT
22 SendStrategies(sender, Initiator, Strat)
23 return S
At the beginning, a game initiator is chosen between
all the mesh routers. The choice can be done randomly or
the most connected node can be elected to avoid multi-
hop transmissions in the network. The initiator picks out
the order by which players will perform the game. Then,
it broadcasts a signal containing the order of the game to
all players informing them to start the game.
Each player picks a random valid strategy and sends
it back to the initiator. After collecting all strategies, the
latter sends them to the first player. This player selects
a random valid strategy in the set of all valid strategies
that it can perform according to the assumptions men-
tioned earlier and which maintains the connectivity of
the network (i.e. The backbone is a connected graph). It
compares it with its current strategy and keeps the one
that keeps the network connected and yields a higher
value of the utility function (i.e. it compares the following
utilities : Ui(Sirand, Si)and Ui(Si(t), Si)). Then, it
sends its new decision (i.e. the chosen strategy), following
the order of the game, to the next player which will do
the same.
The step of selecting an improving strategy or maintain-
ing the previous one is repeated until the stop condition
T is met. Finally, the last player will send the set of
final strategies S to the initiator of the game. This step
is needed in case we want to further improve the CA of
the network. In fact, the CA procedure can be dynamic.
Thus, our algorithm can be repeated when needed. It can
restart at the 7th line of the algorithm with the actual set of
strategies S instead of restarting the strategies to random
ones. In general, this step can be seen as a negotiation
phase on a common channel before actually affecting
channels to the interfaces.
It is worth noting that the IGCA algorithm may some-
times not reach the global-optimum if one player is
trapped in a local-optimal NE value since more than one
NE can exist.
IV. PER FO RM AN CE EVALUATION
In this section, we evaluate the performance of our
game theoretic algorithm. We first present the simulation
environment and describe the used scenarios. Then, we
describe the results of our experiments. We used the
interference-aware NPOCA [3] algorithm as baseline to
which IGCA improvements are compared. Note that,
although NPOCA was designed to work with partially
overlapped channels, it is supposed to perform well with
a non-overlapping channels environment.
A. Simulation Environment
We consider in our experiments a random wireless
mesh backbone network consisting of 10 mesh routers
placed in a 100m×100mfield. The network uses the
IEEE.802.11a standard as wireless technology and pos-
sesses 8 non-overlapping channels. We shift the number
of nodes’ radios from 2 to 5. The break parameter T is
fixed to 1000 iterations. All nodes have the same commu-
nication range CR = 30m. The interference range (IR) is
estimated as follows: IR = 1.5×CR. Simulations were
performed using 50 different seeds regarding a specific
random node distribution. It is worth noting that to test
the scalability of our proposal, we conducted the same
experimentation on a 50 nodes’ backbone. However, due
to space limitation, we only present here the results of 10
nodes simulations.
B. Performance Metrics
In order to evaluate the performance of IGCA, we have
considered the following metrics:
Connectivity degree: the connectivity degree of a node
iwith reference to the channel allocation is equal
to the number of neighbors that can communicate
directly with node iusing a common channel.
Interference Degree: the interference degree of a MR
is defined as the number of interfering neighbors
regarding the chosen CA scheme.
Channel distribution: the channel distribution of a
channel cCis equal to the number of all interfaces
assigned to channel c. In other words, it is the
number of nodes that can use channel cto send data.
Number of possible simultaneous connections: It is equal
to the number of possible connections that can
be handled simultaneously on non-interfering links
using a specific channel cC.
C. Simulation Results
In what follows, we present our simulation results in
terms of connectivity degree, interference degree, channel
distribution, and number of possible simultaneous con-
nections.
1) Connectivity Degree:
This evaluation metric is fundamental for the good de-
ployment of any wireless network. It is important to note
that connectivity is well addressed by our algorithm since
IGCA returns always a connected graph. Nevertheless,
NPOCA algorithm can provide a non-connected network
graph. Hence, in order to conduct a fair comparison
between the utility functions of both approaches, we
added the connectivity condition to NPOCA.
In the first set of experiments, we studied the perfor-
mance of our proposed IGCA algorithm in terms of Cu-
mulative Distributed Function (CDF) of node connectivity
degree.
Figs. 1(a) and 1(b) show the impact of the number
of interfaces per node on the connectivity degree. From
that figure, we can observe that with 2 and 3 radios per
node, NPOCA gives slightly better results than IGCA.
In fact, unlike our gain metric Gi, the metric used in
the utility function of the NPOCA algorithm is based on
two topology control factors (i.e. the hop count from the
node to the gateway and a connectivity factor set to 1
if the node can reach the gateway) which warrant the
efficiency of network links toward the gateway. But still,
our approach gives a good node connectivity degree and
the improvement of NPOCA is merely of 1 node more. In
addition, we notice that starting from 4 interfaces, IGCA
converges to the best connectivity scheme with regards to
the same random 10 nodes distribution in the network.
2) Interference Degree:
Fig. 2 shows the CDF of interference degree for both
IGRA and NPOCA algorithms. Note that this perfor-
mance metric is closely related to the previous one and
(a) I= 2 (b) I= 3 (c) I= 4 (d) I= 5
Figure 1. CDF of connectivity degree in a 10 nodes backbone with CR=30m
(a) I= 2 (b) I= 3 (c) I= 4 (d) I= 5
Figure 2. CDF of interference degree in a 10 nodes backbone with CR=30m
(a) I= 2 (b) I= 3 (c) I= 4 (d) I= 5
Figure 3. Channel distribution in a 10 nodes backbone with CR=30m
IGCA NPOCA
Channel Identifier I= 2 I= 3 I= 4 I= 5 I= 2 I= 3 I= 4 I= 5
1 0.62 1 1.16 1.32 0.34 0.7 0.76 1.08
2 0.44 0.98 1.14 1.5 0.44 0.72 0.92 1.18
3 0.62 0.98 1.12 1.46 0.52 0.84 1.02 1.1
4 0.62 1.02 1.2 1.32 0.44 0.46 1.02 1.4
5 0.46 1.04 1.22 1.62 0.4 0.82 1.02 1.16
6 0.54 1 1.18 1.54 0.48 0.62 0.98 1.3
7 0.54 1.1 1.18 1.44 0.42 0.58 1.1 1.4
8 0.64 1 1.12 1.44 0.48 0.62 0.88 1.22
Average per number of interfaces 0.56 1.015 1.165 1.45 0.44 0.67 0.9625 1.23
Average possible communications in the network 4.64 8.12 9.32 11.6 3.52 5.36 7.7 9.84
Table I
AVERAGE NUMBER OF POSSIBLE SIMULTANEOUS CONNECTIONS PER CHANNEL ON A 10 NODES MESH BACKBONE
a good interpretation must be done with reference to the
both.
From Fig. 2(a), we can notice that, using the IGCA
algorithm, 80% of MRs interfere with 3 nodes or less
while they interfere with the double (i.e. 6 nodes or
less) using NPOCA. Recall that in this same scenario,
80% of nodes are connected to 2 nodes or less with
IGCA and to 3 nodes or less with NPOCA (see Fig.
1(a)). With a same reasoning, we observe that 80% and
60% of the backbone nodes, having respectively 2 and 3
interfaces per node, have at most 1
2of interfering nodes
that can carry transmission data using NPOCA. While,
using IGCA, at most 2
3of them can.
In addition, we observe that the improvement achieved
by IGCA in node interference degree can exceed 50% com-
pared to NPOCA when I63. This can be explained by
the good design of our interference-aware utility function
that strengthens the interference awareness of the nodes.
However, we notice that the performance of IGCA de-
grades and becomes closer to NPOCA for high values of
I (i.e., when the number of radios increases and exceeds 5
in our case). This is simply because, with considering high
number of interfaces per node, the interference becomes
unavoidable whatever the CA scheme used.
3) Channel Distribution:
Fig. 3 further investigates how the assigned channels
are distributed within the network. Note that in our
simulations, 8 channels per radio interface are available.
Recall that this performance metric indicates the average
number of radio interfaces assigned to use the same
channel for transmitting data. From this figure, we can
clearly observe the unrelenting fairness of IGCA ap-
proach in distributing interfaces between channels. This is
very important to avoid having underused and overused
channels in the network. Whereas, channel distribution
graphs related to NPOCA become more serrated when
the number of radios per node increases.
4) Number of possible simultaneous connections:
To further show the benefit of our approach, we plot
in Table I the average number of possible simultaneous
connections, i.e., the average number of non-interfering
links per channel using the aforementioned scenarios. We
can clearly observe that the average number of possible
communications in the network is improved using IGCA
in comparison with NPOCA and this result is indepen-
dent from the number of interfaces per node. The gain
is up to 51%. In addition, the values given by IGCA for
every specific value of Iare smoother than those given by
NPOCA. Hence, besides being fair in distributing radios
on channels, IGCA enables equitable number of possible
transmissions over channels.
V. CONCLUSION
In this paper, we have envisioned a new channel as-
signment algorithm for Multi-Radio Multi-Channel Wire-
less Mesh Networks. We proposed an interference-aware
channel assignment algorithm based on a potential game,
called IGCA, which intends to alleviate the interference
experienced by the Mesh routers (MRs) and maintains the
connectivity of the network. To gauge the effectiveness
of our proposal, numerous simulations were performed.
We evaluated the potential performance gains of IGCA
and proved that it achieves significant gains. We ap-
preciate how much our proposed CA game theoretic
algorithm contributes in minimizing interference between
neighbors, generates a fair distribution of nodes’ radios
between the available network channels and, above all,
allows a better number of simultaneous connections in
the network in comparison with a prominent game-based
approach: the NPOCA algorithm.
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... With the above limitations, it is necessary to design a CA scheme that assigns channels with minimum interference among the routers and provides maximum connectivity. The existing CA schemes [3,4,5,6,7,8,9,10] have assigned channels to routers with minimum interference and with/without considering network connectivity. So we propose a channel assignment scheme that is dynamic, distributive, and operates with simplistic operations and ensures minimum interference with maximum connectivity. ...
... As each player has a dissimilar satisfaction level, this scheme involves a lot of complex computation. The solution in [7] models the CA problem as a semi-distributed cooperative game. Solution tries to minimize network interference. ...
... Through the simulation results, we compare our proposed distributed CA scheme INCACG with two CA schemes called Interference-aware Game-based CA (IGCA) [7] and link-preserving interference-minimization (LPIM) [9]. LPIM has modeled the CA problem as a non-cooperative game. ...
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Wireless mesh network (WMN) with wireless backhaul technology provides last-mile Internet connectivity to the end-users. In multi-radio multi-channel WMN (MRMC-WMN), routers provide multiple concurrent transmissions among end-users. The existence of interference among concurrent transmissions severely degrades the network performance. A well-organized channel assignment (CA) scheme significantly alleviates the interference effect. But in trying to minimize interference, the CA scheme may affect the network connectivity. So, the CA scheme has to consider both these two conflicting issues. In this paper, as part of the initial configuration of WMNs, we propose a game theory-based load-unaware CA scheme to minimize the co-channel interference and to maximize the network connectivity. To adapt to the varying network traffic, we propose a dynamic channel assignment scheme. This scheme measures the traffic-load condition of the working channels of each node. Whenever a node finds an overloaded channel, it initiates a channel switch. Channel switching based on the fixed threshold may result in a channel over/underutilization. For optimal channel utilization, we propose a fuzzy logic-based approach to compute the channel switch threshold. The contending nodes and their densities and loads dominantly affect the network capacity and hence the performance. In the context of network capacity enhancement, we have addressed these factors and focused on increasing the network capacity. The simulation results indicate that our proposed load-unaware and load-aware CA schemes outperform the other related load-unaware and load-aware CA approaches.
... Bezzina et al. proposed an algorithm named Interference-Aware Game Based Channel Assignment Algorithm (IGCA) [31]. They have formulated the problem based on the game theoretic approach. ...
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