An Effective Hybrid GACSA-based Multi-user Detection for Ultra-wideBand Communications Systems.
ABSTRACT In this paper, we investigate the performance of various interference cancellation techniques in direct-sequence ultra-wideband (DS-UWB) communication systems. Multiple access interference (MAI) causes the performance of the conventional single user detector in DS-UWB systems to degrade. Due to high complexity of the optimum multiuser detector, suboptimal multiuser detectors with less complexity and reasonable performance have received considerable attention. A hybrid approach that employs a genetic algorithm (GA) and chaos algorithm (CSA) for the MUD problem in UWB communication systems is proposed. By taking advantage of heuristic values and the collective intelligence of GACSA, the proposed detector offers almost the same bit error rate (BER) performance as the full-search-based optimum multiuser detector does, while greatly reducing the computational complexity. The near-far resistance of the GACSA-based multiuser detector is also examined. The good behavior of the proposed approach is demonstrated by means of comparisons in term of bit error rate (BER) performance and implementation complexity with the classical Rake receiver and different multiuser receivers previously proposed in the literature on this subject.
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ABSTRACT: This paper introduces a novel two-step interference alignment beamforming algorithm for a multiple-antenna interference channel with uncoordinated interference. The proposed algorithm performs subspace division of the signal space by using the total least squares method to use all available degrees of freedom in the system. The algorithm also uses the minimum mean square error as a criterion to maximize the sum rate of the system. Simulation results indicate that the sum rate of the proposed algorithm outperforms the sum rates of previous works in the context of a network with uncoordinated interference. The performance levels of these algorithms are also compared for different uncoordinated interference strengths. Almost the same trend is obtained for the sum rate performance.Cognitive Computation 01/2013; 5(2). · 1.10 Impact Factor
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An Effective Hybrid GACSA -based Multi-user Detection for Ultra-WideBand
Communications Systems
Jyh-Horng Wen
Department of Electrical Engineering,
Tunghai University
No. 181, Sec. 3, Taichung Harbor Rd.,
Taichung, Taiwan, R.O.C.
e-mail :jhwen@thu.edu.tw
Ho-Lung Hung, Chien-Chi Chao
Department of Electrical Engineering,
Chienkuo Technology University,
Changhua, Taiwan.
e-mail: hlh@cc.ctu.edu.tw
Chia-Hsin Cheng
Department of Electrical Engineering,
National Formosa University
No. 64, Wunhua Rd., Huwie, Yunlin
632, Taiwan,
e-mail:chcheng@nfu.edu.tw
Abstract—In this paper, we investigate the performance
of various interference cancellation techniques in direct-
sequence ultra-wideband (DS-UWB) communication
systems. Multiple access interference (MAI) causes the
performance of the conventional single user detector in
DS-UWB systems to degrade. Due to high complexity of
the optimum multiuser detector, suboptimal multiuser
detectorswith less complexity
performance have received considerable attention. A
hybrid approach that employs a genetic algorithm (GA)
and chaos algorithm (CSA) for the MUD problem in
UWB communication systems is proposed. By taking
advantageof heuristic values and the
intelligence of GACSA, the proposed detector offers
almost the same bit error rate (BER) performance as the
full-search-based optimum multiuser detector does,
while greatly reducing the computational complexity.
The near–far resistance of the GACSA-based multiuser
detector is also examined. The good behavior of the
proposed approach is demonstrated by means of
comparisons in term of bit error rate (BER)
performance and implementation complexity with the
classical Rake receiver and different multiuser receivers
previously proposed in the literature on this subject.
and reasonable
collective
Keywords-Ultra-wideband, genetic algorithm, chaos
alhorithm, multiuser detection
I.
INTRODUCTION
Ultra- wideband (UWB) technology is currently being
investigated as a promising solution for short range, high-
capacitywireless communications
conventional radio systems, UWB systems have a number
of advantages that make them attractive for consumer
communication applications, such as low cost, low power
consumption, low complexity, and high data rate
transmission, etc [1-2]. To realize the multiple accesses
technique in UWB systems, two commonly used approaches
is time hopping (TH) and direct sequence (DS) techniques
[3-4]. When using DS technique, pseudo-random code is
applied to spread the data bit into multiple chips, just as in
systems.Unlike
conventional DS code division multiple assess (DS-CDMA)
systems, and the users are separated by independent spread.
In the DS-UWB communication system, multiple access
interference (MAI) is the main source of interference.
Additionally, it is well known that MAI limits DS-UWB
system capacity. Multiuser detection (MUD) is a powerful
technique to combat MAI and to improve the performance
of UWB systems. Considering the large complexity
involved in optimal multiuser detection (OMUD), which is
exponential in the number of active users, most of the
current work is centered around investigating suboptimal
approaches.
Some sub-optimal approaches, such as differential
receive and adaptive receivers were proposed [5-6]. These
techniques do not require channel estimation and allow
capturing a large amount of the received energy. To reduce
the complexity of this optimum
suboptimum multiuser have been developed in the past
several years [4], [5-12]. For examples, the minimum-mean-
squared error (MMSE) MUD has been described in [5],
while an interference cancellation (IC) based MUD has been
proposed in [6] [9]. The traditional receiver for such a UWB
system is a simple matched filter [1-2] and the performance
is degraded due to the MAI and ISI. Moreover, using
intelligent computation techniques seems to be a feasible
approach to achieve a bit error rate (BER) performance
close to that of the OMUD when reducing computational
complexity. Hybrids MUD have been proposed [7-8] as
have other distinct approaches [9]. While each has its own
merits and drawbacks, we will focus on a multistage
approach which performs parallel interference cancellation
(PIC) at each stage.
In resent years, there are many new intelligence MUD
techniques which utilize some genetic algorithms (GA) [10-
14], particle swarm optimization (PSO) [15], and neural
networks (NN) [16] have lower computational complexity.
In all kinds of techniques, the evolutionary computation
algorithm has proven to be an effective way to design the
sub-optimum multiuser detectors. In [9-10], it is shown that
GA based MUD approaches the single-user performance
bound at a lower complexity as compared with optimal
detector various
Digital Object Identifier: 10.4108/ICST.WICON2010.8624
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maximum likelihood (ML) optimum detector. GA can get
the optimum solution for multidimensional engineering
problems. Furthermore, GA algorithm has suffered from
some deficiencies. It is well known that premature
convergence degrades the performance of GA and reduces
the search ability [11]. In addition, a change in the genetic
population through generation results in the destruction of
previous knowledge of the problem [12].
The GA is a stochastic search algorithm whose
procedures are based on the Darwinian models of natural
selection and evolution [4]. Given some arbitrary initial
solutions, the GA will generate the better solution through a
series of genetic operations including selection, crossover,
and mutation. Furthermore, the GA searches the solution
space in parallel, that is, a set of possible solutions are
manipulated in the same generation, so multiple local
optimum can be reached simultaneously and thereby the
likelihood of finding the global optimum is increased. The
genetic algorithm based multiuser detector (GA-MUD) [11-
13] is one of the sub-optimum multiuser detectors that
evolves from the OMUD by replacing the exhaustive search
scheme with the GA [11], and has attracted much attention
in recent years. By means of GA’s powerful search ability,
the GA-MUD can attain sub-optimum performance with
less complexity compared to the OMUD [12]. One of the
proposed GA-MUDs uses the GA to provide a good initial
point for the successive stage of the multistage detectors. A
modified GA that adjusts the operations of the GA to
improve the BER performance is proposed in [13]. However,
the performance of the GA-MUD still can not approach a
sub-optimal, which is far from the single user bound, at high
system load. Thus, after a low-complexity GA detector, the
parallel interference cancellation (PIC) scheme [10] is used
to simultaneously subtract the interference from each user’s
received signal. However, at heavy system load, the
multistage conventional PIC (CPIC) approach suffers
performance degradation due to a poor cancellation, which
is brought about by the relatively high error rate of bit
decisions in the preceding stage [12]. Thus, the partial
cancellation contrarily is a better policy than the complete
cancellation [13]. Consequently, in this paper, we proposed
a low-complexity iterative MUD approach for DS-UWB
communication systems which can effectively alleviate the
harmful effects of MAI. The proposed method is based upon
the use of iterative processing techniques, which have
already been successfully applied to
communication system and, in particular, to the MUD case.
Using this approach, the CSA is embedded into the GA to
improve further the fitness of the population at each
generation. Such a hybridization of the GA the CSA reduces
its computation complexity by providing faster convergence.
The remainder of this correspondence is organized as
follows. Section II describes the signal model of the DS-
UWB system and Section III introduces the proposed
particle swarm optimization technique, and multiuser
detection based on the GA-CSA technique. Section IV,
much wireless
simulation results
performance of the proposed detector. Conclusion and
discussion are given in Section V.
areprovided to demonstrate the
II.SYETEM AND CHANNEL MODEL
A. Transmitter Model
In this section describes a simple model for a DS-UWB
communication system employing multiuser detector which
will be employed for the proposed of analysis in this paper.
We assume a K-users DS-UWB system over the UWB
indoor multipath fading channels, where each user employs
unique DS spreading code. The transmitted signal qk(t) for
the kth user is obtained by spreading a set of binary phase-
shift keying (BPSK) data symbol {bk[i]} onto a spreading
waveform sk(t), which is written as follows:
P
q tE b i s t
?
where Ek is the symbol energy of the kth user, P is the
packet size,
? ?
[ ]1
is the ith data symbol of the kth
user, and Tb is the symbol interval duration. The spreading
waveform sk(t) is also written as follows:
1
1
( )(
k k n
n
G
?
N
Gc
?
of the kth user, Nc is the chip numbers, Tc is the chip
interval duration, and
( ) w t
duration Tc= Tb / Nc.
1
( )[ ] (),
kkkkb
i
iT
??
?
(1)
kb i ? ?
,
0
),
c
N
?
c
s tc w tnT
?
??
(2)
where
2
k n
,
1
c
n
??
, k=1,2,…,K,
? ?
? ?
,
1
k n
c
is the nth chip
is the chip waveform of
B. Multipath Channel Model
In this paper, we use the IEEE 802.15.3a indoor channel
model, which is based on a modified Saleh-Valenzuela (S-V)
model where multipath components arrive in clusters, each
of which could contain several components namely rays
[17]. For the UWB indoor transmission environment, the
channel impulse response of UWB indoor channel model is
modeled as
k L
h tt
? ??
?
?
,,
1
( )()
k k lk l
l
??
?
??
,
1
(1),
k L
k lc
l
tlT
? ?
?
???
(3)
where Lk denotes the total number of propagation paths of
the kth user,
, k l
?
is the channel coefficient of the lth path of
?
is the multipath delay of the lth path of
the kth user. In this thesis, we suppose that the multipath
delay
, k l
?
is an integral multiple of Tc, L1 = L2= … = LK =
L, and the system is assumed to be synchronous. In system
performanceanalysis, the commonly adopted UWB
the kth user and
, k l
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multipath channel models are those standard statistical
models established by IEEE 802,15.3a task group, i.e.,
CM1~CM4. These standard models are based on modified
S-V model, of which the clustered multipath components
obey a lognormal amplitude distribution and an equi-
probable polarity distribution.
C.
communication Systems
This section
conventional multiuser detector and optimum MUD for
UWB communication systems. When passing the signal
through the indoor environment, the obstacles in the
transmitted path will cause the multipath transmission.
Therefore, the total received signal can be formulated as
follows:
Review of Multiuser Detectors in DS-UWB
describes the construction of the
1
( )r t ( ) ( )( )
K
kk
k
q t h tn t
?
???
?
11
[ ] ( b i v t)( ), n t
KP
kkkb
ki
E iT
??
???
??
(4)
where ? is linear convolution, n(t) is zero-mean additive
white Gaussian noise and
( )
k
v t
as template signal of the kth user, which is a convolution
between the kth user’s spreading code and channel
coefficient.
( )( )
kk
s th t
??
is defined
D. Conventional Detector
The template signal vk(t) that is transmitted over a
channel is corrupted by channel noise. Hence, the function
of the receiver must detect the template signal vk(t) for each
user. According to [5], we note that a filter which is
matched to a template signal vk(t) of duration (Nc+L-1)Tc is
characterized by an impulse response. The channel response
for kth user can be written as follows:
*
,( )( ).
opt kk
htvt
??
(5)
So, the output of the filter which is matched to a template
signal vk(t) can be written as follows:
( ) ( )( )
kopt k
y tr tht
??
( )()
k
r tvt
???
KP
E b i v tiT
??
and the discrete-time impulse response sampling at t = iTb is
represented as follows:
[ ]().
kkb
y iy iT
?
Then the discrete-time received signal after sampling (iTb) is
written as follows:
KP
y iEbj v ij
??
,
*
*
k
*
k
11
[ ]()() ( )(),
mmmb
mi
vtn tvt
??? ? ???
??
(6)
(7)
*
k
*
k
11
[ ] [ ] [] [ ]
? ?
[ ]n i [ ]
?
kmmm
mj
vivi
????
??
,
11
[ ]j R[ , ]i j[ ]
KP
mmm kk
mj
Ebn i
?
??
??
??
,,
1
desired signal
ISI
[ ][ , ]
i i
[ ] [ , ]
i j
P
kk k kkkk k
j
j i
?
E b i R
??? ??? ?
Eb i R
?
??
?
??? ? ???? ?
,
11
MAI
[ ]j R [ , ]i j[ ],
KP
mmm kk
m
m k
?
???? ? ????? ?
j
Eb n i
?
??
??
??
(8)
where
*
k
,[ , ]
m k
R
[ ]
k
n i
?
[][ ],
?
m
i j
?
v i
?
jvi
??
?
?
*
k
[ ]n i[ ].vi
Hence, the signal that received by a CD can be detected:
ˆ
[ ] sgn
k
bi
?
where a CD structure is shown in Fig. 1.
??
[ ] ,
y i
CD
k
(9)
E. Maximum Likelihood Detector
The optimum MUD performs maximum-likelihood
sequence detection jointly across all users’ sequences [5-6].
According to [4-6], the optimal multiuser detector can be
achieved by maximum a posteriori (MAP) estimation.
Because the probability of bk[i] = +1 is equal to the that of
bk[i] = -1, the maximum likelihood (ML) estimation can be
generalized by the MAP estimation. As a result, the optimal
multiuser detector that fulfils ML sequence estimation [5]
gives the best performance. However, its computational
complexity which grows exponentially with the number of
the users forbids application in real system. The search for b
is a combinatorial optimization problem detailed in [5]
whose complexity grows exponentially with K. Though
impractical given large K, its performance establishes a
benchmark for multiuser design. In UWB systems, the
number of zero cross-correlation entries in R can very be
large and can help decouple the ML sequence detection
problem into much smaller, independent ML sequence
detection problems.
ML
ˆ
arg max2
KP
? ? ?
?
b
??
[ 1, 1]
.
TT
??
?
??
b b Ay b ARA b
(10)
III.GENETIC ALGORITHM AND CHAOS ALGORITHM
1.Solution Representation
For the subsequent genetic operations, the trial solution to
the addressed problem must be encoded into the string form
first. An encoded solution is referred to as a chromosome
and its elements are referred to as the genes. The multiuser
detection can be regarded as an optimization problem that
finds the most likely combination of the binary transmitted
bits
pOMD,
. Since the configuration of the trial solution
ˆ
,,
ˆ
,
ˆ
[
,2,1,pKpp
bbb
?
is already an antipodal binary string of
ˆb
]
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length K, the encoding process is unnecessary.
2.Initialization
For each time the GA being carried out, a chromosome
set with Pc members named the chromosome population is
created in order to produce the better solutions by applying
the genetic operations, where Pc is known as the population
size. Generally, the larger the population size, the faster the
convergencerate but the higher the computational
complexity. In this paper, the seed chromosome in the initial
population is created by the Rake receicer. The Rake
receiver first makes
]
ˆ
,,
ˆ
,
ˆ
[
ˆ
2,1 K,pp ,pp
bbb
?
?
b
according
equivalent baseband signal r(i) in (10) and then passes its
output to the GA for further processes.
a rough
to
decision
received the
3.
In the cost evaluation process the GA employs the
problem-dependent objective function to evaluate the cost or
fitness of a solution, which represents how closely the
chromosome fits the addressed problem. A high fitness or
low cost reflects the excellence of the chromosome. The
objective of our system is to find the bˆ that has the
minimum cost. Consequently, we define the cost function of
a chromosome for the ith bit duration as
.
[ 1, 1]
? ? ?
?
b
Cost Evaluation
??
ML
ˆ
b
arg max2.
KP
TT
??
?
??
b Ay bARA b
(11)
4.Selection
Selection is the operation that chooses the chromosomes
from the parent population to constitute a selected
population for crossover. Since the parent chromosomes
with advantageous genes are more likely to produce the
better offspring chromosomes, only certain parent
chromosomes will have the chance to produce the offspring
chromosomes. The selected population consists of Pc
chromosomes chosen from the parent population with
probabilities that are inversely proportional to their costs by
using the roulette wheel selection scheme [11]. Then, the
selection rate of the kth chromosome is
?
?
j
?
?
?
c P
j
k
S,k
C
C
P
1
1
1
)
ˆ(
b
)
ˆ(
b
, (12)
where
)
ˆ(
j
C b
is the cost of the jth chromosome.
5.Crossover
The crossover is the operation that exchanges the genes
of two chromosomes to generate a new pair of offspring
chromosomes. Through
chromosomes are expected to be superior to their producer
because they inherit the merits of both parents. Three
commonly used crossover schemes are one-point, multi-
point, and uniform crossover [4]. The simplest crossover
crossover, the offspring
scheme, the one-point crossover, is utilized in this study.
The probability that the crossover operation is applied to the
selected chromosomes, which is known as the crossover rate,
is set to 1.
6.Offspring Mutation
The mutation is the operation that randomly alters the
genes of the crossover results for increasing the diversity of
genes. Sometimes the members of the selected population
are not diverse enough to find any better solution even if the
global optimum is not reached yet. This situation is known
as the premature convergence and may happen when one of
the parents has a relatively low cost compared to the others',
in which this outstanding chromosome predominates the
selected population, resulting in a considerable amount of
identical offspring chromosomes. Therefore, the mutation
operation is introduced to prevent the premature
convergence. The GA with a high mutation rate Pm is more
likely to escape from the local optimum but has the slower
convergence. Conventionally, the mutation rate Pm is
usually smaller than the crossover rate. In this paper, the Pm
is set by 0.1 to compromise between the optimization and
convergence.
7.Elitist Replacement
The mutation and crossover are the operations that
modify genes within chromosomes. To avoid destroying the
good solutions during the mutation and crossover, we
replace a small portion of offspring population with the
good chromosomes in the parent population. This is called
the elitist replacement. In
replacement is implemented by selecting the best Pc
chromosomes from the combination of the parent and
offspring population [11].
this paper, the elitist
8.Iteration
Unlike the other sub-optimum multiuser detectors, the
GA is an iteration scheme. We can repeat the procedures
mentioned in parts 3~7 to refine our solution. In GA, each
iteration is called a generation. It is known that when the
pre-defined generation number Gn is reached the iteration
will be terminated and the best chromosome in the last
generation will be taken as the result of the detection. With
the aid of the elitist replacement, the best chromosome of
the offspring population is never worse than that of the
parent population. This ensures the discovery of better
solution after certain generations.
The standard adaptive genetic algorithm (AGA) is
proposed by Srinvas [11]. its main idea is that when the
fitness values of population tend to convergence, the
probability of the occurrence of the genetic operators will be
increased so as to avoid the premature convergence, where
when the fitness values of population tend to divergence, the
probability of the occurrence of the genetic operator will be
decreased so as to converge to the optimum. Figure 1 the flow
chart of the GA-based detection scheme for DS-UWB systems.
Digital Object Identifier: 10.4108/ICST.WICON2010.8624
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The probabilities of crossover and mutation are defined as
follows:
??
?
??
?
??
?
??
?
?
?
?
?
?
?
?
?
avge
?
f
avge
avge
c
ffe
ff
ff
ff
e
p
,
,
3
max
max
1
( 13 )
??
??
??
??
?
?
?
?
avge
avge
avge
m
ffe
ff
f
ff
e
p
,
,
4
max
max
2
(14 )
where
average fitness value of the population, respectively. f ? is
the larger one of the fitness values of two individuals to be
crossed, and f it is the fitness value of the individual to be
mutated.
321
,,eee
and
the range [0,1].
The basic idea of AGA is that , during the genetic search
process, when the individual fitness value is less than the
average fitness values of the population, larger rates of
crossover and mutation operators should be adopted, and
vice versa. This scheme can prevent the genetic search
process from prematurely converging to local optimal
solutions by regulating the balance between exploration and
exploitation in the solution space. The procedure of AGA
can be depicted as follows:
Step 1: Initialize the population and various parameters.
Step 2: Calculate the fitness values including individual
fitness values and average fitness values of the population.
Step 3: Compute the crossover and mutation rates
according to formulas (13 ) and (14 ).
Step 4: execute genetic operation including selection,
crossover and mutation.
Step 5: when the current generations is less than the
maximal generation, turn to step 2; otherwise, terminate the
program and return the optimal searching. Adaptive Genetic
algorithm combined with chaos searching [17] (GACSA):
The proposed approach GACSA is developed on the
basis of the standard GA by introducing chaos searching and
the other set of crossover and mutation rates so as to guide
to the whole population to evolve in the solution space. In
GACSA, the chaos searching algorithm is used to local
exploration for obtaining the local optimum while the AGA
with two sets of crossover and mutation rates is responsible
for global exploitation. In order to fulfill chaos searching,
here, a premature decision identifier ? is employed: assume
max
f
and
avge
f
are the maximal fitness value and
4e are constants predetermined in
that
if
is the average fitness values at generation i , which
1
, where
is computed by ?
?
P
n
i
nf
P
1
i
nf
represents the fitness
value of the nth individual in the ith generation, P is the
population size. At the same time, suppose the best
individual’s fitness is
ifmax, f ? is the average of all the
individuals whose fitness values are greater than
ff
?
??
max
?
crossover and mutation operators have a significant effect
on the convergence of GA during the search process. Thus,
the other set of crossover and mutation rates adopted in this
paper is devised with ? as follows:
?
??
exp(1
e
if . And
then let
i
. It is known that the rates of
??
?
??
?
??
???
??
?
??
???
)exp(1
1
03 . 003. 0
)
1
3 . 01
2
1
?
?
e
p
p
mut
c
(15 )
where
The detailed procedure of GACSA is described as follows:
Step 1: Initialize the population and various parameters.
Step 2: Calculate the fitness values of each individual and
the premature decision identifier? .
Step 3: When ? is greater or equal to
generation (GEN) is greater than maximal generation (M),
M/2, carry out the following steps for chaos searching.
1). Let d=1.
2). Execute chaos search from step 2 to 5 as shown in
procedure chaos optimization algorithm, and the rest (D-1)
individuals keep invariant.
3).
1
?? dd
. If d reaches D, terminate the chaos search;
otherwise, turn to 2) to optimize the next variable. When ?
? and GEN is less or equal to M/2,
calculate the rates of crossover and mutation operators by
formulas 4) and 5), and carry out the corresponding genetic
operations. Otherwise, turn to Step 4.
Step 4: Calculate the crossover and mutation rates according
to equations (13 ) and (14).
Step 5: Execute genetic operations including selection,
crossover and mutation.
Step 6: When the GEN is smaller than the maximal
generation (M), turn to Step 2; otherwise, terminate the
program and return the optimal solutions.
1e and
2e are the positive numbers predetermined.
*
? and current
is greater or equal to
*
IV.
SIMULATION RESULTS
In this section, the simulations of multiuser transmission for
DS-UWB radio systems under the modified S-V channel
that GACSA based MUD algorithm is adopted are shown in
Figs. 2-5. The UWB CM 1-4 which are discussed in paper
indicate the different transmission distance for indoor
environment, and all Rake receivers is adopted for CM 1-4.
We assume that the packet size is 4 bits and the number of
users is 10 on DS-UWB systems
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Generate Initial Population
p
bˆ
Evaluate Cost
Start with g = 0
Chromosomes Selection
No
Yes
Crossover to Generate New Chromosomes
g = g+1
Mutate the New Chromosomes
End
g > Gn
Fig. 1. The flow chart of the GA-based detection scheme for DS-
UWB systems.
. Fig. 2 shows the performance of GACSA based MUD,
the optimum detector, and several suboptimum detectors,
for K=10. The performance of HNN detector for DS-UWB
systems is better with the increase of iteration. However, the
performance improvement is no longer obvious when it
achieves about 100 iterations. That is because the HNN
detector has many local minimum, but it is unable to
determine which minimum is the global minimum. Since
the information of R is known for HNN detector, the
performance of HNN detector always is better than CD
when it achieves about 50 iterations. Unfortunately, the
performance of HNN detector is poorer than original GA,
original PSO, GACSA and OMUD detectors for DS-UWB
in CM 1. On the other hand, as for the BER of the original
GA-based MUD, an error floor is observed foe the results
show in the figure. This is because of the limitations of the
GA associated with the particular set of individual and
generation values. In contrast, the BER of the proposed
GACSA based MUD demonstrated a perfect approach
similarto that of OMUD
complexity compared with that of the original PSO- and
GA-based MUD.
Further, in Figs.3-5 the performance of GACSA based
detector is depicted for CM 2, CM 3 and CM 4, respectively.
The performance of HNN detector is approximated other
suboptimum detectors at SNR=0-6dB for other DS-UWB
channel models. But, its performance is worse at SNR=8-
12dB for others. The neuron output of HNN detector with
sign activation is either +1 or -1, but the neuron output of
HNNdetector is distributed form -1 to +1. As can be
observed in Figs. 2-4, the performance of our algorithm is
better than that of all existing suboptimum schemes with
same level of complexity. Moreover, the performance of
GACSA detector is approximated OMUD at SNR=6-12dB
for other UWB channel models.
with less computational
V.
CONCLUSION
To reduce computational complexity of the optimal
multi-user detector, a novel hybrid algorithm that employs
GA and CSA is present. In this paper, we proposed a new
suboptimum multiuser detector for DS-UWB systems,
which utilizes a hybrid algorithm to decide on the
transmitted bits. Using this approach, the CSA is embedded
into GA to improve further the fitness of the population at
each generation. Such a hybridization of the GA with the
CSA based MUD reduces its computational complexity by
providing faster convergence. The complexity of the
proposed is approximately
(O KS
that its performance is significantly better and more robust
comparedto other examined
Simulations results are provide to show that the proposed
detector has significant performance improvement over the
detectors based on HNN, PSO and CD in term of MAI.
())
Tpi
PP
?
, and it is evident
suboptimum schemes.
Fig.. 2 The simulation of BER for DS-UWB systems that employs GACSA,
CD, GA, PSO, ML and HNN detectors with UWB CM 1when K=10
Fig. 3 The simulation of BER for DS-UWB systems that employs GACSA,
CD, GA, PSO, ML and HNN detectors with UWB CM 2
Digital Object Identifier: 10.4108/ICST.WICON2010.8624
http://dx.doi.org/10.4108/ICST.WICON2010.8624
Page 7
Fig. 4 The simulation of BER for DS-UWB systems that employs GACSA,
CD, GA, PSO, ML and HNN detectors with UWB CM 3
Fig.5 The simulation of BER for DS-UWB systems that employs GACSA,
CD, GA, PSO,ML and HNN detectors with UWB CM 4
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Digital Object Identifier: 10.4108/ICST.WICON2010.8624
http://dx.doi.org/10.4108/ICST.WICON2010.8624
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