1. 978-1-4799-3400-3/13/$31.00 © 2013 361
An Intelligent Link Adaptation Scheme for OFDM based Hyperlans
1,2,4
Atta-ur-Rahman 3
Muhammad Hammad Salam 2,4
Muhammad Tahir Naseem 2,4
Muhammad Zeeshan Muzaffar
1
Barani Institute of Information Technology (BIIT), PMAS Arid Agriculture University, Rawalpindi, Pakistan
2
Institute of Signals, Systems and Soft-Computing (ISSS), Islamabad, Pakistan
3
The University of Lahore, Islamabad Campus, Blue Area, Islamabad, Pakistan
4
School of Engineering & Applied Sciences (SEAS), ISRA University, Islamabad Campus, Pakistan
ataurahman@biit.edu.pk hammad.salam@yahoo.com tahir.naseem@biit.edu.pk zeeshan.muzaffar@isra.edu.pk
Abstract— Adaptive communication is becoming requirement
of every communication system for effective utilization of
available radio resources. In this approach the transmission
parameters like code rate, modulation size and available power
are dynamically chosen so that the overall system throughput
is maximized while certain constraints like bit error rate are
satisfied. In this paper a similar constrained optimization
problem is solved by optimally choosing the said parameters
with the help of Differential Evolution algorithm with a Fuzzy
Rule Based System (DE-FRBS) in an OFDM environment. in
this proposal the FRBS is used to adapt the code rate and the
modulation scheme according to channel state information
(CSI) and desired quality of service (QoS) while DE is
employed to find the optimum power vector (OPV) to be
transmitted over OFDM subcarriers. Product codes and
Quadrature Amplitude Modulation (QAM) are used as coding
and modulation schemes respectively. Product codes are
considered much powerful in terms of error correction
capability. Significance of the proposed scheme is shown by the
simulations.
Keywords-component; DE, OFDM; FRBS; BER; ACM
I. INTRODUCTION
For effective resource allocation, adaptive communication
has been used over more than two decades. Due to mobility
and enhanced data rate requirement in wireless 3rd
Generation (3G) and 4th
Generation (4G) systems, adaptive
communication has gain more significance. With the help of
efficient adaptive algorithm, transmitter adequately adapts
various transmission parameters according to variable CSI.
For orthogonal frequency division multiplexing (OFDM)
systems, adaptive communication is becoming irresistible
because there are a number of subcarriers, each having a
different CSI and different power requirement. Using a fixed
set of transmission parameters would result in poor resource
utilization since a particular modulation code pair and a fixed
power may not guarantee for the link sustenance. In long
term evolution (LTE) such channels are chosen to shut
down. So here adaptive communication plays its role.
OFDM is base of many advanced wireless communication
standards like IEEE 802.11n (WIFI) [1] and IEEE 802.16/e
(WiMAX) [2].
In literature many adaptive power and bit loading
techniques have been proposed. In 1989 [3], Kallet proposed
adaptive modulation for OFDM systems with the absence of
a practical channel code. Chow et al. [4] investigated
adaptive modulation technique for Gaussian slowly varying
dispersive channels and found it quite applicable. The same
idea of adaptive modulation for wideband radio channel was
investigated by Cyzlwik in 1996 [5]. In 2010, Sastry et al [6]
proposed adaptive modulation using Fuzzy Logic interface.
In this approach fuzzy logic was used to adapt the
modulation symbol based upon the received signal to noise
ratio (SNR). A turbo coded adaptive modulation scheme was
proposed by Hanzo et al [7], where there signal to noise ratio
(SNR) thresholds were used for adapting modulation.
Li et al. [8] addressed a coded bit and power loading
problem for single antenna OFDM systems, where Low
Density Parity Check (LDPC) codes were examined. This
idea was originally motivated by [9]. Al-Askary [10],
proposed an adaptive coding and modulation technique for
OFMD Hyperlan/2 standard, in which modulation was fixed
while different codes rates were chosen for the subcarriers.
Evolutionary algorithms are also investigated for adaptive
communication like a Genetic Algorithm (GA) based
adaptive resource allocation scheme was proposed by [11],
to increase the user data rate where water-filling principle
was used as a fitness function. Moreover, it was shown that
chromosome length helps to achieve optimum power
requirement. In [12] authors proposed an adaptive coding
and modulation technique using FRBS while the transmit
power was fixed. In [13], authors proposed a GA and FRBS
based adaptive communication technique for OFDM based
Hyperlans. The idea was originally motivated by [14], a
work done by same authors, where GA and FRBS were used
for adaptive communication in a WIFI environment.
In [15], an adaptive coding, modulation and power
scheme was proposed, in which non-recursive convolutional
codes were used as forward error correction (FEC) codes. In
this paper hybrid intelligent techniques are investigated for
adaptive radio resource allocation. In [16], authors proposed
adaptive coding and modulation scheme for multi-input
multi-output (MIMO) OFDM systems using machine
learning framework that helps in predicting the modulation
and code rate based upon past observations and CSI. This
was accomplished by a post processor that predicts the
coding ad modulation scheme for next transmission interval.
In [17], authors proposed an outer loop link adaptation for bit
interleaved coded modulation BICM-OFDM using adaptive
kernel regression for learning which after sufficient learning,
able to predict the transmission parameters. In [18], a data
driven approach to link adaptation is used where machine
learning classifier is used to select the modulation and code

2. 362 2013 International Conference of Soft Computing and Pattern Recognition (SoCPaR)
rate. The term data driven is used in a sense that this scheme
requires a learning phase. A number of examples are
generated by different channel scenarios and then the
classifier is trained sufficiently prior to use.
In this paper, an adaptive coding, modulation and power
scheme is proposed in which FRBS dynamically select the
best modulation code pair while Differential Evolution
algorithm selects the optimum power vector according the
CSI at OFDM system such that the overall throughput of the
system can be maximized, while certain constraints should
be satisfied.
Rest of the paper is organized as follows. System model
is given in section 2; section 3 contains coded modulation
and their simulations; section 4 contains rate optimization
criteria and cost function to be optimized, in section 5 FRBS
creation steps and parameters are explained, in section 6 a
brief description of Differential Evolution algorithm is
given; simulation results of proposed scheme are depicted in
section 7 while section 8 concludes the paper.
II. SYSTEM MODEL
The system model considered is OFDM equivalent
baseband model with N number of subcarriers. It is assumed
that complete channel state information (CSI) is known at
both transmitter and receiver. The frequency domain
representation of system is given by
. . ; k 1,2,......,k k k k ky h p x z N= + = (1)
where ky , kh , kp , kx and kz denote received signal,
channel coefficient, transmit amplitude, transmit symbol and
the Gaussian noise of subcarrier 1,2,......,k N= ,
respectively. The overall transmit power of the system is
1
N
total kk
P p=
= ∑ and the noise distribution is complex
Gaussian with zero mean and unit variance. Later in this
paper, the role of same and different power levels will be
highlighted.
It is assumed that signal transmitted on the kth subcarrier
is propagated over an independent non-dispersive single-path
Rayleigh Fading channel and where each subcarrier faces a
different amount of fading independent of each other.
III. PARAMETERS BEING ADAPTED
A. Coding Scheme
Coding schemes used for this framework are set of
product codes. Product codes were originally proposed
by [19]. Since product codes are matrix codes, where
rows contain one code and column contains another
code. The set of row codes and column codes used in
this paper are listed in table1. All of these constituent
codes are BCH codes.
A Modified Iterative Decoding Algorithm [20] is
employed for sake of decoding of these codes. Code
rates are taken from the following set whose parameters
are shown in table-1.
TABLE I. CANDIDATE PRODUCT CODES
Sr.
Row
Code
Column
Code
Product Code
Code
rate
Error
Correcting
Capability
C1 [63,63,1] [63,63,1] [3969,3969,1] 1 0
C2 [63,57,3] [63,63,1] [3969,3591,3] 0.9 1
C3 [63,51,5] [63,63,1] [3969,3213,5] 0.8 2
C4 [63,36,11] [63,63,1] [3969,2268,11] 0.57 5
C5 [63,63,1] [63,57,3] [3969,3591,3] 0.9 1
C6 [63,57,3] [63,57,3] [3969,3249,9] 0.82 4
C7 [63,51,5] [63,57,3] [3969,2907,15] 0.73 7
C8 [63,36,11] [63,57,3] [3969,2052,33] 0.51 16
Figure 2: Performance of different QAM schemes using C1 product code
Figure 3: Performance of different QAM schemes using C6 product code
In the first four codes, column code is considered as rate one
means no column redundancy while in last four codes
redundancy is included in column code.
B. Modulation Scheme
The modulation scheme used for this experiment is
Quadrature Amplitude Modulation (QAM) which is
used by many OFDM standards [1] [2]. Following set of
modulation symbols is used. That is

3. 3632013 International Conference of Soft Computing and Pattern Recognition (SoCPaR)
{2,4,8,16,32,64,128}M = (2)
So with these coding and modulation sets there are 56
possible modulation code pairs (MCP). Performance of
all possible combinations of modulation code pairs
(Equ-8) is obtained some of these results are shown in
fig-2 and 3.
C. Power
It is the third parameter being adapted. Transmitting the
fixed and equal power to all subcarriers is not a good idea
for varying channel profiles. Differential evolution
algorithm is used for finding the optimum power vector
to be transmitted over the OFDM sub-channels.
IV. RATE OPTIMIZATION
In order to maximize the rate for OFDM system following
constrained optimization problem is considered. This can be
stated as,
“Maximize the overall data rate of OFDM system by
optimal selection of modulation and code rate, such that
the target bit error rate and total transmit remain
constrained”.
Mathematically it can be written as;
2
1
max log ( )
s.t,
and (3)
Total
Total T
N
Total i T
i
R R M
BER BER
P p P
=
=
≤
= <∑
where R is the code rate of product code being used from set
C and M is the size of modulation symbol used from set M.
TP is the available transmit power and ip is power
transmitted per subcarrier. TBER is average target BER for
OFDM symbol that depends upon a specific quality of
service (QoS) request or application demand in the
Hyperlan/2 environment. The supported QoS are
4 3 2 1
10 ,10 ,10 ,10TBER − − − −
= while N is total number of
subcarriers in OFDM system.
Figure 4. Proposed system model for adapting parameters
The above cost function is optimized by the proposed Fuzzy
Rule Base System and Differential Evolution. The adaptive
paradigm is shown in fig-4.
V. THE FUZZY RULE BASE SYSTEM
A fuzzy rule base system (FRBS) is proposed, which is
capable of deciding the best modulation code pair (MCP) for
the next transmission interval, based upon the received SNR
and quality of service (QoS) demand at the moment. The
steps and components involved in creation of FRBS are
given subsequently.
A. Obtaining Graphs
Graphs for different combinations of Codes and
Modulation schemes are obtained few of them are
plotted in fig-2 and 3.
B. Data Acquisition
Data is obtained from the graphs in terms of input/output
(IO) pairs. This is taken by drawing the horizontal lines
for various BER and then points of intersection of these
lines with the curves are noted in a look up table that is
used for creation of FRBS.
C. Rule Formulation
Rules for each pair are obtained by the appropriate fuzzy
set used. That is by putting complete pair in input/output
set and a rule generated for each pair.
D. Elimination of Conflicting Rule
The rules having same IF part but different THEN parts
are known as conflicting rules. This appears when more
than one modulation code pair (MCP) are available for
given specification. In this case the most appropriate
choice is selected.
E. Fuzzy Rule Base Creation
Using the Lookup table in above phase Fuzzy Rule Base
is created using Fuzzy Logic Toolbox in MATLAB. In
the creation of FBRS, following components were used,
• Triangular membership function used for
fuzzification
• Mamdani Inference Engine (MIE) is used, that will
decide that which combination of inputs will be
mapped on to which output value
• Standard center average defuzzifier (CAD) is used
VI. DIFFERENTIAL EVOLUTION ALGORITHM
Differential Evolution algorithm belongs to the class of
evolutionary algorithm. It is a famous meta-heuristic
technique that is applied for the solution of various
problems where search space is quite large and optimum
solution is hard to find. It was originally proposed by Storn
& Price [21]. DE is one way or the other resembles Genetic
Algorithms but has a faster convergence rate if compared. In
OFDM PHY
Transmitter
OFDM
Channel
PHY layer
Receiver
Link Adaptation using Fuzzy
Rule Based System (FRBS) and
Differential Evolution Algorithm
Quality of Service
(QoS) Demand/
Subcarrier
Feedback Channel
Sub-channel Estimates
New
Modulation
Code rate
Power

4. 364 2013 International Conference of Soft Computing and Pattern Recognition (SoCPaR)
this scheme it is proposed for finding the optimum power
vector that maximizes the overall throughput of the OFDM
System while satisfying the total power constraint as well as
target bit error rate constraints.
Fig-5 contains the block diagram of fitness function being
applied for sake of finding the fitness of a chromosome
(transmit power vector) in DE algorithm. So the power
vector with the highest fitness (throughput) would be chosen
for transmission. The fitness function can be written
mathematically as;
1
( , )
N
i i
i
R FRBS RSNR QoS
=
= ∑ (4)
Where iRSNR is received signal to noise ratio from ith
subcarrier and and iQoS is quality of service demand at ith
subcarrier. Upon receiving the signal at receiver, channel
estimates are found, and then based on these estimates and
QoS demands, optimum vector is found.
Figure 5. Fitness Block
Figure 6. The Differential Evolution Algorithm
The algorithm used is described in fig-6. This is
accomplished in following manner that according to a given
set of channel coefficients (alphas), and the quality of
service vector as input, transmit power vector is perturbed
and using DE, the fittest vector is found and according
modulation code pairs are used that will result in the highest
throughput. The total power constraint is satisfied by
making the choice of upper and lower bounds of candidate
power vector around the fixed power. In this way the
average power transmitted always fulfills the target power
constraint.
VII. RESULTS
In this section proposed scheme is compared with the
other adaptive techniques as well as HYPERLAN/2
standard. The simulation parameters are given in table-II.
In fig-7, the proposed scheme is compared for various
target bit error rates (that is QoS) like average BER=10e-1,
10e-2, 10e-3 and 10e-4 with fixed transmit power. Every
subcarrier was having same and fixed power. In this way
QoS was fixed initially then depending upon the received
signal to noise ratio (SNR), most appropriate modulation
code pair (MCP) was chosen using Fuzzy Rule Base System
(FRBS), then the product of modulation rate and code rate
so called modulation-code-product (throughput) is plotted.
It can be seen that for poor QoS throughput reaches
4.5bits/s/Hz while for high QoS the throughput approaches
2bits/s/Hz. This is because when the BER is relaxed,
relatively high code rate and a larger constellation is chosen
by the FRBS which results in a high data rate. In case of a
tight BER requirement, relatively low code rate product
code with a smaller constellation will be chosen by FRBS,
which results in a poor throughput.
Figure 7. Comparison of proposed scheme for various QoS in a
HYPERLAN/2 environment with fixed power
In fig-8, proposed scheme plotted for various QoS but
with adaptive power. DE was used to choose the power per
subcarrier depending upon the CSI and average QoS
required. A significance difference can be seen between
Quality of Service Vector Q
TransmitPowerVector(P)
2
1α
2
2α
2
Nα
FuzzyRuleBaseSystem(FRBS)
2(log )R M
Throughput
Start
Initialization
Mutation
Crossover
Decision
Taken
Yes
Criteria met?
Selection based upon FRBS
End
No

5. 3652013 International Conference of Soft Computing and Pattern Recognition (SoCPaR)
fixed and adaptive powers. In particular there is about 0.5
bits per second per hertz difference in fixed and adaptive
power. For a relaxed QoS demand (BER=10e-1),
throughput reaches to 5.6 bits/s/Hz at a transmit power of
30dB while in case of a high QoS demand (BER=10e-4),
the throughput reaches to 3.0 bits/s/Hz at 30dB of total
transmit power.
Figure 8. Comparison of proposed scheme for various QoS in a
HYPERLAN/2 environment
In fig-9 proposed scheme is compared with the adaptive
coding and modulation scheme proposed by Al-Askary in
[10] in which the adaptation was based upon SNR based
coding and modulation thresholds. As simulation results
reveal, proposed scheme profoundly performs better than
that of proposed by Al-Askary as well as HYPERLAN/2
standard. According to this figure, the proposed scheme with
adaptive power is on the top in terms of bit rate. For
example, at 30dB of the total transmit power it reaches to
100Mbps, which is a standard rate at wired local area
network (LAN). In terms of performance, the next scheme is
adaptive coding and modulation scheme with a fixed
transmit power, that reaches at the rate of 80Mbps for a 30dB
of the total transmit power.
The adaptive coding and modulation scheme proposed by
Al-Askary approaches a data rate of 70Mbps and 60Mbps at
30dB transmit power, for the cases without and with
redundant channels respectively. The standard Hyperlan/2
data rate with fixed parameters approaches at the 54Mbps for
30dB transmit power.
In this simulation, two cases are investigated; in the first
case, those product codes are employed that do not use any
channel for redundancy. This means rate one column codes
are used. In the second case, product codes with rate below
one column codes are used, in which some channels are
simply carrying the redundancy bits or redundant
information. From the simulations it is observed that the
product codes having column code with redundancy perform
better than the former case.
This fact is not surprising that redundancy in the code
rate always plays an important role in terms of bit error rate
while the code rate is compromised. In this way certain
channels are sacrificed for carrying just redundant
information not the useful one, but on the other hand it helps
in mitigating the bit error rate.
The case with zero channel redundancy is poor in terms
of bit error rate while it is offering more bit rate. In fig-9, we
have employed the case of non-redundant sub-channel as
explained earlier.
Figure 9. Comparison of proposed scheme with different schemes of
same area
TABLE II. SIMULATION PARAMETERS
Sr Parameter name Value
1 Coding Schemes Product Code
2 Code rates 1, 0.9, 0.8, 0.57
3 Modulation Schemes 2, 4, 8, 16, 32, 64, 128 QAM
4 Bits/symbols in modulation 1, 2, 3, 4, 5, 6, 7
5 Total MCPs 4x7=28
5 OFDM Standard used HYPERLAN/2
6 Number of subchannel 63
7 Minimum throughput MCP 0.57x1=0.57bits/s/Hz
8 Maximum throughput MCP 1x7=7bits/s/Hz
9 Parameters being adapted Modulation, code and power
10 Adaptation Criteria DE, Fuzzy Rule Base System
VIII. CONCLUSIONS
An adaptive coding, modulation and power scheme is
proposed using Differential Evolution (DE) algorithm in
association with a Fuzzy Rule Based System (FRBS). In this
proposal FRBS is used for adaptive coding and modulation
and DE is used for adaptive power. Moreover, FRBS is used
as a fitness function for DE. The proposed scheme is
compared with fixed and variable power scenarios as well as
with OFDM HYPERLAN/2 standard and other remarkable
work-done in same field. It is found that proposed scheme
significantly performs better than others in terms of channel
capacity and data rate.
ACKNOWLEDGEMENTS
This research work was partially funded by Barani
Institute of Information Technology (BIIT), Rawalpindi,
Pakistan.

6. 366 2013 International Conference of Soft Computing and Pattern Recognition (SoCPaR)
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