Communications and Network, 2013, 5, 22-26
http://dx.doi.org/10.4236/cn.2013.53B2005 Published Online September 2013 (http://www.scirp.org/journal/cn)
Cognitive Radio Spectrum Allocation Strategy Based on
Improved Genetic Algorithm
Bin Hou, Yunxiao Zu, Weihai Li, Gang Liu, Junjie Ding
School of Electronic Engineering, Beijing University of Post & Telecommunication, Beijing China
Received March, 2013
ABSTRACT
With the rapid development of wireless communication industry, shortage situation of spectrum resource is increasingly
significant. It has become an important topic to study cognitive radio spectrum allocation algorithm that is of higher
spectrum utilization ratio, less system power consumption and better algorithm efficiency. Analyzes spectrum allocation
models based on genetic algorithm, and then puts forward new improved genetic algorithm. The algorithm adopts niche
crowding operation to avoid individual inbreeding. It adaptively adjusts crossover and mutation probability to keep
them always in the appropriate state. It provides more equal individual competition opportunity by hierarchical meas-
ures, which can effectively avert premature convergence to local optimal solution. It obviously improves the district's
total transfer rate on the premise that it has met the requirements of minimum user transfer rate and limitations of max-
imum total power and maximum bit error rate. Simulation results prove the effectiveness of the proposed algorithm.
Keywords: Cognitive Radio; Spectrum Allocation ; Genetic Algorithm; Niche; Adaption
1. Introduction
Under the background of increasingly shortage of spec-
trum resource, realization of rational allocation for cross
band spectrum and utilized techno logy of cognitive radio
(CR) are becoming focus of research in wireless commu-
nication arhjyjea. Though cognitive radio has achieved
high attention from researchers, many key technology
problems remain to be solved and there is still a long way
to go for commercial application.
Due to the fact that CR network s are made up of plen-
tiful high density distribution of base stations, sensor
nodes and users, resource allocation should take fully
into account parameters such as free carrier quantity, rate
needs of users, channel quality, bit error rate constraints
and transmission power, etc. But with the growth of users
and subcarrier number, complexity of resource allocation
problems in cognitive radio networks is increasing ex-
ponentially. Traditional resource allocation methods can
no longer meet demand [1]. Besides, Equipment power,
bit error rates and computational complexity o f cognitive
radio network are restricted owing to its properties. In
this case, better resource allocation methods are required
to ensure transmission quality and network performance
and improve network cap acity and transfer rate of cogni-
tive radio network.
This paper puts forward a kind of cognitive radio
spectrum allocation strategy based on improved genetic
algorithm. The simulation results indicate that under the
circumstances of same system total power, minimum
user rate and bit error rate, this proposed allocation algo-
rithm is superior to traditional common genetic algorithm
on spectrum efficiency.
2. Improved Genetic Algorithm
Genetic Algorithm (GA) is a kind of algorithm that imi-
tates biological evolution mechanism to search global
optimal solution to target problem. It puts the potential
solution in problem space as a biological individual, and
encodes the individual. It imitates biological evolution
process, carries out selection, crossover and mutation
operation circularly and finally chooses the individual of
optimal performance as the solution to a problem [2].
Genetic algorithm is appropriate to large and complex
search problems, and cognitive radio spectrum allocation
is such a problem—when allocating spectrum, there is an
enormous quantity of available distribution schemes. It’s
unpractical for enumerative search, and input parameters
influence each other. Thus, genetic algorithm can be applied
to research on cognitive radio spectrum allocation issue.
Consider problems existing in traditional genetic algo-
rithm as follows: (a) towards the end of algorithm run-
ning, individual genetic is approximate and their off-
spring cause inbreeding, thus resulting in population ge-
netic redundancy. (b ) Crossover and mutatio n probability
C
opyright © 2013 SciRes. CN

B. HOU ET AL. 23
is fixed, which influences algorithm speed. (c) The popu-
lation is single. If there exists some individual whose
fitness is more outstanding in the initial population, then
it’s easy to converge to the local optimal solution [2, 5].
The shortage of the traditio nal genetic algorithm is modi-
fied and improved in this paper.
In view of problem (a), adopt niche crowding opera-
tion to remove individuals that have similar gene.
In view of problem (b), adaptively adjust crossover
and mutation probability to keep them always in the op-
timal range.
In view of problem (c), take stratified measures to di-
vide the population into more child population. Inde-
pendently run low-level genetic algorithm in each child
population. When running up to certain algebra, merge
the child population and then turn to high-level genetic
algorithm. Thereby reduce occurrence probability of lo-
cal optimum solution.
3. Spectrum Allocation Model Design
3.1. Scene Design
Suppose a cell cellular radius is 1 km and a base station
tower’ height is 40 m. Apply shadow fading model and
supposes that shadow effect doesn’t change with time.
The channel gain of user m at subcarrier n is ,mn dB.
The average power spectral density of WGN (white
Gaussian noise) at the receiving end is W/Hz. Ac-
cording to empirical value, set the value of 0 to 10-12
W/Hz [2]. The number of available subcarriers in a cell
and total users is 2000 and 50. Every subcarrier band-
width is 30 KHz. Descending frequency band is 1900-
1930MHz and ascending frequency band is 1950-1980
MHz. Adopt MQAM (Multiple Quadrature Amplitude
Modulation) modulation and permissible highest modu-
lation order is 256. The MQAM modulation order and
transmission power of user m at subcarrier n are ,mn
and ,mn. Bit period ,mn equals to 100us. If a subcar-
rier has not been used by any user, then set ,mn to 0.
Tolerable maximum BER (bit error rate) of every user is
e, it set to 0.0005%. Minimum rate demand min
R
equals to 64 Kbits/s. Maximum transmission power
of base station in a cell is 80W.
b
W
0
NN
W
P
x
T
P
ma
G
3.2. Channel Gain Matrix Design
Suppose the channel gain of user m at subcarrier n is
dB. It is presented by matrix B.
,mn
b
1,1 1,21,1 1,
2,12,2 2,12,
,
1,1 1,21,1 1,
,1,1, 1,
NN
NN
mn
MMMN MN
M MMNMN
bbb b
bbb b
b B
bbb b
bbb b


 














Formula (2) is for path loss value of free space[3].
(dB) 32.45+10lg20lg
L
fd (2)
In the formula, f represents the center frequency of
carrier and d represents the distance (km) between receiv-
ing antenna and transmitting antenna.
Considering shadow fading effect, channel gain is ex-
pressed as Formula (3) [3].
(dB) s(dB)
,
mn
bL

 (3)
In the formula,
s

is level variation produced by
shadow effect. When under simulation, assume that
s

is a normal distribution random variable with 0 dB mean.
In macro-cellular, the standard deviation

ranges from
5 dB to 12 dB. In the micro-cellular, the standard devia-
tion

ranges from 4 dB to 13 dB. The typical value of

is 8 dB and it is set to 8 dB for simulation.
3.3. Target of Resource Allocation Solution
The target of CR resource allocation discussed in this
paper is described as follows. On the basis of channel
gain of each subcarrier, determine which subcarrier be
allocated to which users, transmission power of each
subcarrier and modulation order of MQAM to achieve
total maximum rate of the CR system. In addition, the
following constraint conditions should be satisfied. 1)
The transfer rate of each user should be higher than
min imu m rat e min Mbits/s, as high as possible. 2) BER
of each user is lower than the maximum BER e, as low
as possible. 3) The system total power consumption is
less than power consumption upper limit dBm, as
little as possible.
RP
xma
G
3.4. Fitness Function Design
According to the target of resource allocation, designed
fitness function is delivered by formula (4):
,
11
2,
,
()
2
NM
Am
nm
mn
mn
fA DS
log W
S



 n
(4)








(1)
()
A
f
A stands for fitness of resource allocation scheme
A. D is bandwidth of subcarrier. ,mn Represents spec-
trum utilization and ,mn represents MQAM modula-
tion order of user m at subcarrier n. Therefore, the value
of fitness function is system total transfer rate.
S
M
The system should meet the three constraints described
in 3.3. If anyone of them is not satisfied, then set the fit-
ness function value of the individual to 0, resulting that it
cannot participate in the generation of next population.
Hence we can get individual fitness function modified
from constraints.

B. HOU ET AL.
24
,,
11 1
,( ( ,))
0,
NM N
mnmnmin me
nm n
DSDSRPPgABG
 
 




 ,,
Others
()
A
fA
max
(5)
4. CR Resource Allocation Based on
M improved
and Child Population
Formlays matrix coding form adopted by in-
Improved Genetic Algorithm
ain steps of CR spectrum allocation based on
genetic algorithm are individual coding, child population
division, niche crowding operation, excluding individuals
that don’t accord with constraint conditions, selection,
crossover and mutation of low-level genetic algorithm,
adaptive adjustment of low-level algorithm parameters,
selection, crossover and mutation of high-level genetic
algorithm and adaptive adjustment of high-level algo-
rithm parameters.
4.1. Individual Coding
Division
ula (6) disp
dividuals.
Figure 1. CR Spectrum Allocation Flow Chart Based on
Improved Genetic Algorithm.
1,1 1,2
aa 1, 11,
2,12,22,12,
,
1,11,21, 11,
,1,1, 1,
,,,
=( )
NN
NN
mn
MM MNMN
MMMNMN
mnmn mn
a a
aaa a
a A
aaa a
aaaa
aWP


 






















，

(6)
Matrix A represents one kind of spectrum resource al-
lo
ubca
cation mode. There are M user facilities and N subcar-
riers in CR system. ,mn W represents MQAM modula-
tion order and ,mn Presents transmission power of
user m at srrier n in CR system. The two parameters
are randomly generated, and each subcarrier can at most
be occupied by one user facility. There are only six mod-
ulation modes of every subcarrier. The items in expres-
sion: ,{0,16,32,64,128,256}
mn
W
rep

are on behalf of no
, 64QAM, 128QAM and
256QAM modulation.
When the algorithm begi
use, usi
ns running, first it randomly
ge
ng 16MQAM, 32QA
nerates
X
Y

initial individuals with matrix A for-
mat. Theyitute the initial population P the algo-
rithm. Then P divided i nt o X child population
const


12 1
,,'
XX
PP PP


by line. Every child population includes Y individu-
4.2. Selection, Crossover and Mutation rithm
Low
i
P
als.
Operation of Low-level Genetic Algo
-level genetic algorithm is meant for child popula-
tion. When individual fitness of each resource allocation
way is figured out, the probability of the i-th individual
,
x
i
A
being selected in child population
,1 ,2,
[, ]
x
xx xY
PAA A


is determined by the following formula[. 3]
,
(
Axi
fA

,
,
1
)
() ()
selectlowx iY
Axy
y
pA
fA


 (7)
,
()
Axi
f
A
population stands for the i-th individual fitness of child
x
P. Operate on child population
x
P for
roulette selection based on fitness. The better fitn the
individual of child population owns, the larger probabil-
ity the individual is selected. Then new child population
can form.
After se
ess
lection operation, do crossover operation on
ne
exchange for m, that is, randomly in terchange individuals
w population. The crossover operation of low-level
genetic algorithm uses single-point crossover. Cross po-
sition is selected by roulette wheel selection. Mutation
operation of low-level genetic algorithm adopts two-line

B. HOU ET AL. 25
from two lines of selected matrix, as shown in Figure 2.
4.3. Niche Crowding Operation of Low-level
Genetic Algorithm
tion,
croseration with the initial non-
Blend the new formed child population after selec
sover and mutation op
iterated child population, forming new child population
with 2Yindividuals. Make use of formula (8) to com-
pute the hamming distance between every two individu-
als.
2
1
1, 2,,1
|| MiMN AA 


|| ()1,2, ,
ij ikjk
k
a ajiiMN

 
 

 (8)
When ||||
ij
A
AL
with lower fitness
, impose Penalty Function o
dividual between n in-
i
A
and
j
A
to re-
du v
ver and
Mutation Probability
)
ce this tness. L is the minimum hamming
distance limit between two individuals, whosealue is
1/3 of average hamming distance of the population. Rank
the child population individuals in descending order de-
pending on new fitness, and then choose the first Y indi-
viduals to compose new population [4].
4.4. Adaptive Adjustment of Crosso
individual’s fi
Formula (9) reveals crossover probability.
(FA
,,
is the crossover probability of individual
,
crosslows isj
A

,
s
i
A
and ,
s
j
A
in population
s
P. 1
on the basis of empirical value. , ()
Asi
k is constant coficsetefient , to 0.1
f
A , (
Asj
fA
is the fitness of cssover individuals. maxA f is the
maximum fitness in
and )
ro
s
P , and minA
fitness. f is the minimum
max ,,
,, 1max min
2()()
AAsiAsj
cross
ff fA
F


(,)
lows isjAA
A
AAkff  (9)
Formula(10) reveals mutation probability. is
stant coefficient , set to 0.02 on the basis of empirical
va
2
kcon-
lue. , ()
Asi
f
A is the fitness of mutation operation in-
dividuals. maxA f is the maximum of individual fitness in
populat minA f is the minimum of individual
fitness. ion, and
,
,2
max min
()
Asi
fA
()
cross lows iAA
FAk
ff
 (10)
4.5. Selection, Crossover and Mutation
Operation of High-level Genetic Algorithm
-
latio -
en-
tia
results. When
BER limit of
N
jN
iN
MN
High-level genetic algorithm is meant for the total popu
n. When low-level genetic algorithm has independ
ently run a certain iterative algebra in each child popula-
tion, merge all child population into total population P.
Do roulette selection operation on P based on fitness.
The crossover operation of high-level genetic algo-
rithm adopts uniform crossover [6]. Every point is pot
l intersection. According to the 0-1 sequence that is
randomly generated and equals the number of individual
DNA bits, two father populations randomly choose indi-
viduals in accordance with corresponding position of 0-1
sequence to generate two child populatio ns.
5. Analysis of Simulation Results
Simulate for 50 times, and take average
there are 50 users in the system and the
each user is 0.0005%, Figure 3 displays the variation of
system transfer rate with traditional genetic algorithm
iteration times. As can be seen, system transfer rate of
traditional genetic algorithm is steadily rising with itera-
tion times, and ultimately converge to a subopti mal value.
By comparison, system transfer rate of improved genetic
algorithm present in this paper is rapidly rising with it-
eration times, and soon reaches a high level. Therefore,
the system total transfer rate obtained from resource al-
location scheme based on improved genetic algorithm is
superior to that of traditional genetic algorithm.
1,1 1,1,1N
aaa 1,
,1 ,,1 ,
,1 ,,1 ,
,1 ,,1 ,
*
ii N j
mutation
jjN i
MMN M
a
aa aa
AA
aa aa
aa aa


 

 





 

 

 

 

 

 

 


  

 

 


Figure 2. Mutation operation of low-level genetic algorithm.
Figure 3. System Total Transfer Rate Comparison Between
Improved Genetic Algorithm and Traditional Genetic Al-
gorithm.

B. HOU ET AL.
26
Figure 4. System Final Total Transfer Rate Variation
Amount of Users.
ays the variation of system final
ans with amount of users when the BER limit of
ea
rum allocation problems in cognitive
r comes up with resource alloca-
7. Acknowledgements
y the Youth Research and
REFERENCES
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trfer rate
ch user is 0.0005% and the amount of total users is 10,
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users in system, the total transfer rates decline due to
congestion. However, if apply the improved algorith m in
this paper, all system total transfer rate obtained from
resource allocation scheme increases significantly com-
pared to traditional genetic algorithm.
6. Conclusions
In allusion to spect
radio network, this pape
tion scheme based on improved genetic algorithm and
constructs steps of the algorithm and related parameters.
The simulation results demonstrate that in comparison to
simple genetic algorithm; under the condition of the
same system total power limit, minimum user rate needs
and maximum BER limit, the system total transfer rate of
improved genetic algorithm increases substantially.
This work was supported b
Innovation Program of the Electronic Engineering
School, BUPT. (Grant No. 2012RC0304 ).
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