Rate adaptive resource allocation in ofdma using bees algorithm

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Special Issue: 15 | Dec-2014 | IWCPS-2014, Available @ http://www.ijret.org 14
RATE ADAPTIVE RESOURCE ALLOCATION IN OFDMA USING
BEES ALGORITHM
Archana C1
, Rejith K.N2
1
PG Scholar, ECE Department, MES College of Engineering, Kerala, India
2
Assoc. Professor, ECE Department, MES College of Engineering, Kerala, India
Abstract
Orthogonal Frequency Division Multiple Access (OFDMA) is the promising access technique for future networks which allows
multiple users to transmit simultaneously. The problem of allocating resources (subcarriers, bit and power) among the
communicating users in multiuser orthogonal frequency division multiplexing (OFDM) system is a constraint optimization
problem. Bio-inspired Networking represents an emerging area to obtain optimal solutions for handling various challenges in the
networking scenario. Nature is our mother and an inspiration from nature always gives the best. Bio-inspired algorithms are
known for their efficiency in solving NP hard problems. This paper focusses on realizing the rate adaptive resource allocation
problem in OFDM systems using bio-inspired approach. The resource allocation problem using Genetic Algorithm, algorithms
based on foraging behavior in ants and flocking behavior in birds have already been modelled. The paper suggests the
application of Bees Algorithm for resource allocation in OFDMA with the goal of maximizing the data rate of each user.
Keywords: OFDMA, Bees Algorithm, Resource allocation, and PSO
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1. INTRODUCTION
Due to the fast development of digital signal processing and
very large scale integrated circuits, wireless communication
systems have been experiencing an explosive growth in the
past decades. Next generation wireless networks are
expected to handle large number of subscribers, while at the
same time deal with the different service requirements of
each user. Thus Orthogonal Frequency Division Multiple
Access (OFDMA) forms the radio resource allocation
scheme for the existing and envisioned networks to support
the increasing number of users with the limited spectrum
level. OFDMA allows several users to transmit
simultaneously at lower data rates. The available spectrum
band is divided into a number of sub-channels and each user
is provided with a disjoint set of subcarriers. After the
subcarrier allocation is determined, the bit and power
allocation algorithm can be applied to each user on its
allocated subcarriers. The user can transmit his data in the
allocated subcarriers. A major challenge in OFDMA is that
for a given number of users and subcarriers, how to allocate
a disjoint set of subcarriers among the users. The classical
approaches for the problem are complex and NP hard.
Researchers have turned their attention towards applying the
nature inspired computational approach for solving the
challenges in networking scenario [1]. Bio-inspired
approach implies transferring knowledge from the natural
systems to the engineering system. The future networks
ranging from nano- scale communication to Inter-planetary
Internet are facing challenges in the communication and
managing aspects. The inherent property of the biological
organisms to self-organize, learn and evolve has inspired the
researchers to address the significant issues in computer
networking. Bio-inspired Algorithms that mimics the
behavior of biological organisms have been used to optimize
complex problems [2] [3].
This paper studies the effect of applying Bees Algorithm
(BA) for rate adaptive resource allocation in an OFDMA
system. The problem of resource allocation in OFDMA can
be handled in two ways: Rate Adaptive (RA) method and
Margin adaptive (MA) method. In rate adaptive method the
resource allocation is optimized by maximizing the data rate
of each user while keeping the power consumption of each
user below an acceptable level. Margin adaptive method
minimizes the maximum power consumption of each user
maintaining a rate constraint. The use of Bees algorithm for
RA based allocation strategy is studied in a single cell
scenario.
2. RELATED WORKS
Genetic algorithm (GA) was the first bio-inspired algorithm
used to study the problem of resource allocation in OFDMA
[4] [5]. But GA can give only sub-optimal results and
includes iterative process. Ant Colony optimization (ACO)
technique has been widely used to address the problem. In
[6], ACO was applied for subcarrier and bit allocation for
margin adaptive problem which provided better
performance in terms of less transmit power consumption
and faster convergence. ACO was compared with GA and
other conventional algorithms for adaptive resource
allocation in OFDMA [7]. But ACO requires more memory
space for computation and a reduction in number of
iterations are required.

_______________________________________________________________________________________
It is widely reported that Particle Swarm Optimization
(PSO) [8] is very easy to implement to solve real world
optimization problems. Gheitanchi et al. [9] has applied
PSO for subcarrier allocation in OFDMA systems with
significant reduction of computational complexity and
increased flexibility compared to conventional techniques;
whereas, PSO resource allocation techniques proposed by
Chakravarthy et al. [10] has improved delay characteristics
while maintaining fairness and throughput utilization. In
[11] PSO was applied for sub-channel allocation in
downlink OFDMA and provided better sum-capacity values.
Ahmed [12] has shown that the Differential Evolution
techniques is better than PSO but takes more time to
converge. The Bees algorithm has not yet applied for the
problem of resource allocation. The algorithm in literature is
mentioned to be a fast converging algorithm and therefore
can be useful for practical systems.
3. SYSTEM MODEL AND PROBLEM
FORMULATION
Consider a single cell with a base station (BS) capable of
serving K users with N subcarriers. In a downlink OFDMA
system, each user estimate channel perfectly and these
estimates are made known to the transmitter via a feedback
channel. These channel estimates are used as inputs to the
resource allocation algorithm that is allowed to run at the
transmitter. The allocation module assigns subcarriers to
each user according to some QoS criteria like rate or BER
request. Serial data from all the users are fed into the
resource allocation block at the transmitter, which then
allocates bits from different users to different sub-channels.
Thus the transmitter applies resource allocation algorithm to
assign different subcarriers to different users and then
determines the bit distribution of the user on each subcarrier
assigned to it. The subcarrier allocation is made known to all
users through separate control channels. The user’s bits are
then modulated into N, M-level QAM symbols which are
combined using IFFT into an OFDMA symbol. This is then
transmitted through a Rayleigh channel with bandwidth B
[13].
Let Rk be the data rate of each user . Then
the resource allocation strategy is based on maximizing Rk
keeping the power consumption of each user below an
allowable budget. is the transmit power of each user. The
channel gain of user k in subcarrier n is denoted by .
Then the objective function for the problem can be
formulated as [14] [15],
maximize 

N
n
nknkk rcR
1
,, (Eq-1)
subject to   nkc nk ,1,0, 


N
n
knknk Ppc
1
,,
Where is the subcarrier allocation indicator such that
if subcarrier n is assigned to user k and 0
otherwise. The number of bits of user k in subcarrier n,
is given by,
 nknknk Hpr ,,2, 1log5.0 
2
2
,
,
n
nk
nk
h
H

 is the sub-channel SNR and is the
noise power spectral density of AWGN channel.
4. BEES ALGORITHM
The Bees Algorithm is a new population-based search
algorithm, first developed by Pham DT in 2005 [16]. The
algorithm is inspired from the food foraging behavior of
honey bees in nature. In its basic version [17] [20], the
algorithm performs a kind of neighborhood search combined
with random search. The processing and collection of food
is highly organized in the honey bee colony. The bees can
move about randomly in search of food. In nature, the food
sources with large amount of nectar should be visited by
more bees.
The bee colony exhibits the property of division of labor.
Each group of bees is given separate designations and they
work accordingly. Generally, the bee colony contains a
single fertile female known as the Queen, a few thousands
of males called Drones and a several thousands of sterile
females called Workers. There are two types of worker bees
namely- scout bees and forager bees. Worker bees are
responsible for the collection of food. The foraging process
begins when the scout bees move about randomly in search
of promising flower patches, which constitutes an
exploration phase. The bees collect information about the
food patch in the exploration phase. By performing the
waggle dance, scout bees share the information about the
direction and distance to patches of flower and the amount
of nectar within this flower with their hive mates. The bees
then evaluate the food patches based on the nectar quality
and energy usage. Thus more foragers will be recruited to
follow that scout bee which carries information that is rated
above a certain quality threshold. Forager bees move on to
exploit the promising flower patches [16] [17].
In the basic Bees Algorithm (BA), one bee is one particle
[16] [18]. A fixed number of bees are created and allocated
randomly for each food source. The fitness at each of these
points is calculated. Bees that have the highest finesses are
chosen as selected bees and sites visited by them are chosen
for neighborhood search. The population of bees around
each food patch is updated in such a way that more number
of bees move towards the food patch with maximum fitness.
The steps are repeated for a fixed number of iterations. In
each iteration the quality of flower exploited by more bees
can deteriorate leading to a change in the allocation pattern
of bees.

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5. PROPOSED SOLUTION
Inspired from the Bees Algorithm, the problem of resource
allocation in OFDMA was handled by considering each bee
as a subcarrier that has to be allocated to a user, who
corresponds to the food patch. The algorithm starts by
initializing a population of subcarriers for each user and then
for each user, for the allocated subcarriers the fitness is
evaluated [18] [19].
The flowchart of BA for resource allocation problem is
shown in figure below.
Fig-1 Flowchart of BA for OFDMA resource allocation.
The steps in the algorithm are as follows:
1. Initialize the parameters including number of users
K, number of sub-channels N, and maximum
number of iterations. Here N=1024, and K was
varied ranging from 8 to 16,
2. Initialize a population of sub-channels (bees) around
each user (food patch),
3. Evaluate the fitness for each user for the subcarriers
allotted (as per Eq-1 ),
4. Sort the list of users in the descending order of their
fitness,
5. While maximum number of iterations not reached,
select a user for neighborhood search,
6. Update the population of bees for the users based on
neighborhood search step,
7. Evaluate the fitness with new population,
8. Run the algorithm until termination criteria is met.
Selecting the sites for neighborhood search [19] is a crucial
step in the resource allocation problem. The aim of OFDMA
technique is to allow several users to transmit
simultaneously at lower data rates and lower transmission
powers. Therefore the algorithm has to select the user with
lowest data rate in the neighborhood search step. This may
be slightly contradictory to what is happening in nature, but
is essential to provide fairness among the users in terms of
power consumption and data rate.
6. SIMULATION RESULTS
The use of Bees Algorithm for rate adaptive resource
allocation in OFDMA was simulated in MATLAB R2013a.
The number of subcarriers considered was fixed at 1024 and
the number of users was varied from 8 to 18. Total power
was assumed to be 1W. The data rate of each user after the
maximum number of iterations is shown in Fig-2 and Fig-3
for 8 and 16 users respectively.
1 2 3 4 5 6 7 8
0
0.5
1
1.5
2
2.5
User index
Capacity(bits/s/Hz)
Bees Algorithm
Fig-2 Capacity versus User index for 8 users.
Every user is allowed to transmit even at lower data rates.
Thus the algorithm is able to support multiuser diversity, as
each user see the channel differently.

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0 2 4 6 8 10 12 14 16 18
0
0.5
1
1.5
2
2.5
User
Capacity(bits/s/Hz)
Fig-3 Capacity versus User index for 16 users.
The working of the algorithm for resource allocation
problem in OFDMA can be validated by observing the
variation in the average data rate for increasing number of
users.
8 10 12 14 16 18
1.38
1.4
1.42
1.44
1.46
1.48
1.5
1.52
1.54
1.56
Number of users
Averagedatarate(bits/s/Hz)
Fig-4 Variation of Average data rate with Number of users
Fig-5 shows the variation of sum-capacity with the number
of users. The obtained result has been compared with the
sum-capacity value obtained by applying PSO on the
problem.
8 10 12 14 16 18
10
15
20
25
Number of users
Sum-capacity(bits/s/Hz)
BA
PSO
Fig-5 Comparison of BA & PSO using Sum capacity versus
Number of users plot.
It is clear that the use of BA for sub-channel allocation for
OFDMA systems has consistently higher sum capacity than
the method using PSO.
0 10 20 30 40 50 60 70 80 90 100
0
2
4
6
8
10
12
14
16
18
20
Number of iterations
Sumcapacity(bits/s/Hz)
BA
PSO
Fig-6 Variation of Sum capacity with Number of iterations
for BA and PSO
In order to analyze the convergence of the algorithm, sum
capacity versus number of iterations was plotted. It is
evident from Fig-6 that the BA converges faster than PSO
when applied for the rate adaptive resource allocation
problem. BA converges at around 20 iterations while PSO
around 40 iterations.
7. CONCLUSIONS
In this paper, the use of Bees algorithm as a new method to
address the problem of rate adaptive resource allocation in
OFDMA is proposed. The proposed method is easy to
implement and the algorithm shows faster convergence in
the rate maximization problem. The simulation results
indicate that this approach is a viable alternative since it has
performance comparable to PSO in terms of achieving
higher total capacities. However the results obtained using
the presented algorithm demands fairness in the data rate
value of users.
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BIOGRAPHIES
The author received the Bachelor degree
in Electronics and Communication
Engineering from Calicut University in
2011. She is currently a PG scholar in
Communication Engineering and Signal
Processing at ECE Department of MES
College of Engineering, Kuttipuram. Her
Project area is Bio-inspired Networking.
The author is currently working as
Associate Professor at ECE Department
in MES College of Engineering. He
received Bachelor degree in Electronics
and Communication from LBS College
of Engineering, Kasaragod in 1998 and
Master degree in Digital Electronics and
Advanced Communication from MIT Manipal in 2008. He
is currently doing PhD under Karpagam University. His area
of research is Bio-inspired Networking.

Rate adaptive resource allocation in ofdma using bees algorithm

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