Dynamic Frequency Selection in OFDMA

Dynamic Frequency Selection in OFDMA
Yuqin Chen, SungHwan Shon, Sang-Jo Yoo, Jae Moung Kim
Dept. of Information and Communication Engineering
INHA University, Incheon, Korea
E-mail: river4416@hotmail.com, kittisn@naver.com, sjyoo@inha.ac.kr, jaekim@inha.ac.kr
Abstract—In OFDM system, dynamic frequency selection is an
approach to efficiently improve the spectrum usage. According
to the channel magnitude response, each user selects the
subcarriers with the highest channel gain. The existing algorithm
divides the whole subcarriers into partitions, and each user
selects one for simplicity. To get better performance, larger
number of partitions with less subcarrier is needed. The existing
algorithm is modified by increasing the successful allocation
probability to allocate more partitions to each user. And to
guarantee cooperation among users, the higher priority to
allocate is given to the user which has the largest amount of deep
fading partitions.
Keywords ⎯ Orthogonal frequency division multiplexing,
frequency selective fading channel, dynamic frequency selection.
1. Introduction
The basic principle of Orthogonal Frequency Division
Multiplexing (OFDM) is to split a high-rate datastream into a
number of lower rate streams that are transmitted
simultaneously over a number of subcarriers. Because the
symbol duration increase for the lower rate parallel subcarriers,
the relative amount of dispersion in time caused by multipath
delay spread is decreased [1]. Due mainly to its ability to
overcome multipath fading, OFDM is being considered as a
promising technique to provide high data rate service.
In conventional systems, the subcarriers that are assigned to
each user are nonadaptively fixed in a pre-determined way.
Therefore, some users would suffer from poor channel effect
resulting from the deep fading and large path loss at certain
time instant. However, the channel qualities for each user are
mutually independent. The subcarriers that appear in deep fade
to one user may not be in deep fade to other users. Therefore,
to improve the system performance and capacity, we need to
allocate spectrum dynamically to each user according to the
channel quality [2].
Futhermore, many papers have proposed the way how to
allocate spectrum in company with power control or adaptive
modulation [3][4]. In [2], a dynamic subcarrier allocation
algorithm is developed and studied in order to improve the
capacity of a multi-user OFDM system in the downlink
environment. The proposed algorithm uses a decentralized
approach and considers the instantaneous channel response of
each user in parallel. In order to reduce the complexity of the
system, the available subcarriers are divided into a number of
partitions and the algorithms attempt to select the partition
with the highest average channel gain. However, situations
may arise whereby two or more users want to select the same
partition. As such, an important aspect of this algorithm is to
resolve such conflicts.
Whereas, this algorithm has some shortage. First of all, as the
number of partitions increases, the probability to successfully
resolve the conflicts seriously decreases. Due to this reason,
the whole subcarriers must be divided into a small amount of
partitions with many subcarriers. What’s more, this algorithm
does not support different data rate for different users.
To improve the performance, we need to divide the
subcarriers into a larger number of partitions. In this paper,
modified methods are proposed to improve system
performance and also to support different data rate based on
the previous allocation scheme. More than one partition are
allocated to each user, and one small skill is used to increase
the sucessful allocation probability. Besides, To co-operate
with other users, allocation priority is considered. Higher
priority is given to a user with worse subcarrier partitions. The
simulation results obtained show that the proposed modified
method outperforms the previous scheme in terms of a lower
bit error rate (BER) for the same signal-to-noise ration (SNR).
This paper is organized as follows. Section 2 shows the
system model used in this paper. The conventional dynamic
spectrum allocation algorithm and the proposed modification
methods are described in Section 3. This is followed by an
evaluation of its performance by computer simulation. Finally
the paper is ended up by a conclusion.
2. System Model
A scheme of the OFDMA system model is shown in Figure.
1. In this figure, K denotes the total number of users and Nfft
denotes the whole number of subcarriers. To simplify the
allocation algorithm, the subcarriers are divided into Np
partitions, and each user is allowed to transmit in one or more
partitions. At the transmitter, the serial datastream from the K
users are fed into the partition allocation block. Using the
channel information from all K users, the partition allocation
algorithm is applied to assign partitions to users. Next, data
modulation is processed. The complex modulated symbols
would be transformed into time domain by IFFT. Cyclic prefix
is inserted in front of each symbol to ensure orthogonality in
multipath environments. The transmitted signal is then
transmitted into different frequency selective fading channels
to different users.
At the receiver, the cyclic prefix is removed to eliminate ISI
and then the time domain samples of K users are transformed
by the FFT block and then are demodulated. The adaptive

allocation information is used to extract the demodulated bits
from the partitions to the K users.
Figure 1. System model for OFDMA system based on adaptive resource
allocation
3. Frequency Allocation algorithm
In some systems, the subcarrier allocation plan is
nonadaptively fixed, which does not take into account the
channel magnitude response of each user varies independently.
Thus certain users may be allocated partitions that suffer from
poor channel gains due to large path loss and serious fading.
As an example, Figure 2 shows the channel magnitude
response for four users, from which we can see for each
subcarrier, some users suffer from serious fading while others
are able to achieve a high channel gain. Thus it is important to
consider the channel magnitude response when allocate the
subcarriers. Furthermore, it is necessary to adopt dynamic
spectrum allocation plan due to the time variety of channel
because of the mobility.
3.1. Conventional Algorithm
In [2], the whole subcarriers are divided into partitions, the
number of which equals to the number of users for simplicity.
Each user acts in parallel and attempts to select the partition
with the highest average channel gain. However, situations
may arise whereby two or more users attempt to select the
same partition.
The process is separated into two phases: initialization
phase and iteration phase. In initialization phase, the necessary
information is assembled and conflicts between users are
resolved by an iteration phase.
H(f)
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Magnitude Response
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61
Subcarrier
User 1
User 2
User 3
User 4
Figure 2. An example of channel magnitude response for four users.
1) Initialization Phase: the whole subcarriers are divided into a
number of partitions and the average channel gain of each
partition is calculated to determine the set of initial usage
value for a particular user. A higher usage value implies a
better channel for user, so a greater probability to select it.
Conversely, a lower usage value represents a worse channel
quality because most of the subcarriers within that partition are
in deep fade and thus should not be selected.
2) Iteration Phase: After initialization, the iteration phase is
carried out to solve the conflict among users. During each
iteration the set of usage values for each user is modified
according to the selected partition of the other users at the
preceding iteration. This is done by calculating the cost of
using each partition for all users. The cost equals to the
number of the other users that have selected the same partition.
In order to resolve the conflict between users for partition
allocation, the usage value should be modified in each
partition. The modified usage value of each partition is based
on the current usage value and the inhibition perceived by the
partition under consideration. The inhibition is directly related
to the cost using the selected partition. Consider the variables
UN(t-1) as the usage value of partition N during the previous
iteration, CN as the cost of using Partition N, K as the total
number of users in the system and w as the weightage factor,
then the usage of Partition N during the current iteration is
defined as follows:
U N ( t −1)
× (1 − w)
U N ( t ) = U N ( t −1)
× w +
C N + 1
( K − 1)
Therefore, the usage value of a partition that is selected by
more than one user would be reduced accordingly. Conversely,
the usage value of a partition what is not selected by any user
would remain unchanged. The above process is repeated until
each partition is only allocated to one user. If the allocation is
(1)

unsuccessful even after the maximum number of iterations, the
partitions are allocated based on some forceful criteria.
The noise factor is used to introduce some randomness in
the usage values so that users would not continuously alternate
between two particular partitions.
The weightage factor determines the weightage that is given
to the usage value of a partition during the iteration when
calculating the usage value of that partition during the current
iteration. This prevents a user from selection with low average
channel gain due to drastic changes in usage values.
3.2. Proposed Methods
To improve the performance, we should divide the
subcarriers into a larger number of partitions and allocate
several smaller partitions instead of one larger partition to each
user. However, it will obviously increase the complexity and
decrease the probability of successful allocation. For example,
in the existing algorithm, when the number of iteration is fixed
as 20, the probability of successful allocation is 98% as the
number of partition is 8; and the probability is reduced further
to 76.5% for 16 partitions. In order to reduce the complexity
and improve the probability of successful allocation, we
propose some modification methods as follow:
Fisrt of all, the simplification of the actual case is pointed
out. We assume all of the subcarriers are used by several users
fairly. The whole subcarriers are divided into partitions which
is lager than the number of users. It implies that the allocation
should be competitive and shareful. One proposed
modification method is following:
Each user assembles the average channel information of the
whole partitions. One user can be allocated more than one
partitions. Then extend the channel information matrix by
duplicating the channel information (the number is larger, the
channel is better) for each user. For example, two and three
smaller partitions are allocated to two users A and B,
respectively. In equation (2), A1 and B1 are the channel
information of each partition to user A and B. A2, B2 and B3
are replicas of A1 and B1. The numbers in equation (2) are
assumed to be the channel information, and the larger number
means that the channel gain of this partition is better. In this
paper, the actually used channel information is the frequency
magnitude response of each subcarrier.
⎡A1 ⎤ ⎡42,15,30,28,8 ⎤
⎢
A
⎥ ⎢
2 42,15,30,28,8
⎥
⎢ ⎥ ⎢ ⎥
R = ⎢B ⎥ 1 = ⎢21,39,16,30,37 ⎥
⎢ ⎥ ⎢ ⎥
⎢B2 ⎥ ⎢21,39,16,30,37 ⎥
⎢B ⎥ ⎢
⎣21,39,16,30,37 ⎥
⎦
⎣ 3 ⎦
Next, Set the largest one in A2 and B2 to zero, and set the
first and second largest one in B3 to zero. It means that if we
have more partitions for user B, the first, second, third and so
on will be set to zero. This method is operated to reduce the
conflicts probability.
In the case of the same number of partition, this method
simplifies the computational complexity without performance
reduction. Meanwhile, the complexity reduction gives us an
(2)
approach to divide the subcarriers into a larger number of
partitions. And we can provide different number of partitions
to different users.
⎡A1 ⎤ ⎡42,15,30,28,8 ⎤
⎢
A
⎥ ⎢
2 0, 15,30,28,8
⎥
⎢ ⎥ ⎢ ⎥
R = ⎢B ⎥ 1 = ⎢21,39,16,30,37 ⎥
⎢ ⎥ ⎢ ⎥
⎢B2 ⎥ ⎢21, 0, 16,30,37 ⎥
⎢B ⎥ ⎢
⎣21, 0, 16,30, 0 ⎥
⎦
⎣ 3 ⎦
In this process, one situation may arise. Most partitions for
one user suffer deep fading, and it is likely that we cannot
avoid allocating one partition in deep fade to this user, so the
performance of this user is unacceptable. In this case, we can
neglect theses partitions by defining the channel information
to zero to show this partition is not available. When allocating
partitions to users, a higher priority is given to this user. It
means we’d better first allocate partitions to this user.
In this method, the available channel information threshold
is needed to define. In equation (4), we can assume that when
the channel information is less than 10, it is unavailable.
Hence B3 has the largest amount of partitions within deep fade,
to avoid allocating the deep fade partitions to B3, we first
allocate partitions to B3.
⎡A1 ⎤ ⎡42,15,30,28,0 ⎤
⎢
A
⎥ ⎢
2 0, 15,30,28,0
⎥
⎢ ⎥ ⎢ ⎥
R = ⎢B ⎥ 1 = ⎢21,39,16,30,37 ⎥
⎢ ⎥ ⎢ ⎥
⎢B2 ⎥ ⎢21, 0, 16,30,37 ⎥
⎢B ⎥ ⎢
⎣21, 0, 16,30, 0 ⎥
⎦
⎣ 3 ⎦
To sum up, we can see that the proposed modification
methods decreases the complexity of each allocation and so
supports dividing the whole subcarriers to more partitions for
users. Consequently it improves the system performance by
the increased partition number. One special case is to separate
each subcarrier as one partition.
If most partitions for one user appear deep fade, to avoid
allocating one deep fade partition to that user, a higher priority
is given to this user and we first allocate partition for it. These
modifications make different data rate available and improves
the system performance.
4. PERFORMANCE ANALYSIS
In this system simulation, an FFT with 64 subcarriers are
allocated to 4, 8, 16, 32 users, respectively. And the proposed
modification methods are simulated in frequency selective
fading channel with AWGN. A multipath slow fading
Rayleigh channel is used as the channel model. Finally, it
should be noted that no coding schemes or power control
methods are employed in the system in order to test the
effectiveness of the proposed algorithm.
(3)
(4)

The subcarrier allocation plan produced by the existing
algorithm is dependent on the average channel gain of each
partition. One way of improving the performance would be to
increase the total number of partitions. Thus, the number of
subcarriers within a single partition would also be reduced.
Consequently, the average channel gain would give a better
indication of the actual channel gain of the subcarriers within
that partition. However, increasing the number of partitions
would also inevitably increase the complexity of the system.
Therefore, it is important to achieve a balance between system
performance and complexity.
BER
1.00E+00
1.00E-01
1.00E-02
1.00E-03
1.00E-04
1.00E-05
1.00E-06
1.00E-07
BER vs SNR for Varying Number of Partitions
4 users, 1 partition per user
8 users, 1 partition per user
16 users, 1 partition per user
32 users, 1 partition per user
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
SNR(dB)
Figure 3. BER vs. SNR for varying Number of Partitions( User
No.=Partition No.)
Figure 3 shows the BER performance of the conventional
algorithm for varying number of partitions. It can be seen that
as the SNR is varied from 0dB to 28dB, an increase in the
number of partitions results in a lower BER for the same SNR
value.
The proposed methods simplify the allocation algorithm by
decreasing the conflict probability. So the successful
allocation probability is increased. The simulation result
shows that when the user number equals to 8, and partition
number is 16, resulting from the modification, the successful
allocation probability is raised up to 76.26% from almost
2.52%. It makes dividing the whole subcarriers to more
partitions with less subcarrier available. Therefore the single
partition gives a better indication of the actual average channel
gain of the subcarriers.
Figure 4 shows the BER performance comparison between
the existing allocation algorithm and the proposed method.
From the figure, we can see for four users, the worst
performance case is the existing algorithm when the partition
number equals to partition number 4. In the proposed method,
the partition number is increased to 8, 16 and 32. The BER
performance is improved obviously as the partition number
increases.
BER
1.00E+00
1.00E-01
1.00E-02
1.00E-03
1.00E-04
BER vs. SNR for Varying Number of Partitions/same Number of Users(4 users)
4 users, 1 partition per user
4 users, 2 partitions per user
4 users, 4 partitions per user
4 users, 8 partitions per user
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
SNR(dB)
Figure 4. BER vs. SNR for varying Number of Partitions with same
Number of Users (User Number=4)
BER
1.00E+00
1.00E-01
1.00E-02
1.00E-03
1.00E-04
1.00E-05
1.00E-06
BER vs. SNR for Varying Number of Partitions(8 users)
8 users, 1 partition per user
8 users,2 partitions per user
8 users, 4 partitions per user
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
SNR(dB)
Figure 5. BER vs. SNR for varying Number of Partitions with same
Number of Users (User Number=8)
BER
1.00E+00
1.00E-01
1.00E-02
1.00E-03
1.00E-04
1.00E-05
BER vs. SNR for Varying Number of Partitions(16 users)
16 users,1 partition per user
16 users, 2 partitions per user
1.00E-06
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
SNR(dB)
Figure 6. BER vs. SNR for varying Number of Partitions with same
Number of Users (User Number=16)

Figure 5 and 6 also show the BER performance comparison
for 8 and 16 users, respectively. These two figures show us
that when the partition number increases to large enough (here
is 32), the performance would not increase any further because
the channel gain is indicated sufficiently enough.
5. Conclusion
In this paper, modification methods to increase the
successful allocation probability are proposed to modify the
existing algorithm. These methods are used in order to divide
the whole subcarriers into a larger number of partitions. Thus,
this augment of partition number reduces the number of
subcarriers within a single partition. Therefore, the average
channel gain would give a better indication of the actual
channel gain of the subcarriers within that partition.
Simulation results describe the BER performance
improvement under the same SNR value. In addition, the
different data rate is supported for users.
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resource allocation techniques for fair scheduling in OFDMA based
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