Wireless Sensor Network, 2010, 2, 472-482
doi:10.4236/wsn.2010.26059 Published Online June 2010 (http://www.SciRP.org/journal/wsn)
Interference Management for DS-CDMA Systems through
Closed-Loop Power Control, Base Station Assignment, and
Beamforming
Mohamad Dosaranian Moghadam1, Hamidreza Bakhshi2, Gholamreza Dadashzadeh2
1Department of Electrical Engineering, Science and Research Branch of Islamic Azad University, Tehran, Iran
2Department of Electrical Engineering, Shahed University, Tehran, Iran
Received April 8, 2010; revised May 15, 2010; accepted May 23, 2010
Abstract
In this paper, we propose a smart step closed-loop power control (SSPC) algorithm and a base station as-
signment method based on minimizing the transmitter power (BSA-MTP) technique in a direct se-
quence-code division multiple access (DS-CDMA) receiver with frequency-selective Rayleigh fading. This
receiver consists of three stages. In the first stage, with constrained least mean squared (CLMS) algorithm,
the desired users’ signal in an arbitrary path is passed and the inter-path interference (IPI) is reduced in other
paths in each RAKE finger. Also in this stage, the multiple access interference (MAI) from other users is
reduced. Thus, the matched filter (MF) can use for more reduction of the IPI and MAI in each RAKE finger
in the second stage. Also in the third stage, the output signals from the matched filters are combined accord-
ing to the conventional maximal ratio combining (MRC) principle and then are fed into the decision circuit
of the desired user. The simulation results indicate that the SSPC algorithm and the BSA-MTP technique can
significantly reduce the network bit error rate (BER) compared to the other methods. Also, we observe that
significant savings in total transmit power (TTP) are possible with our methods.
Keywords: Adaptive Beamforming, Antenna Array, Base Station Assignment, Closed-Loop Power Control,
Constrained LMS, DS-CDMA
1. Introduction
Code-Division Multiple Access (CDMA) for cellular
communication networks requires the implementation
of some forms of adaptive power control. In uplink of
CDMA systems, the maximum number of supportable
users per cell is limited by multipath fading, shadowing,
and near-far effects that cause fluctuations of the received
power at the base station (BS). Two types of power con-
trol are often considered: closed-loop power control and
open-loop power control [1,2]. In a closed-loop power
control, according to the received signal power at a base
station, the base station sends a command to a mobile set
to adjust the transmit power of the mobile. Also, closed-
loop power control is employed to combat fast channel
fluctuations due to fading. Closed-loop algorithms can
effectively compensate fading variations when the power
control updating time is smaller than the correlation time
of the channel. However, in an open-loop power control, a
mobile user adjusts its transmit power according to its
received power in downlink [1-5]. In this paper, an adap-
tive closed-loop power control algorithm is proposed to
compensate for near-far effects.
Diversity and power control are two effective techni-
ques for enhancing the signal-to-interference-plus-noise
ratio (SINR) for wireless networks. Diversity exploits the
random nature of radio propagation by finding indep-
endent (or, at least, highly uncorrelated) signal paths for
communication. If one radio path undergoes a deep fade,
another independent path may have a strong signal. By
having more than one path to select from, the SINR at
the receiver can be improved. The diversity scheme can
be divided into three methods: 1) The space diversity; 2)
The time diversity; 3) The frequency diversity. In these
schemes, the same information is first received (or
transmitted) at different locations (or time slots/freque-
ncy bands). After that, these signals are combined to inc-
rease the received SINR. The antenna array is an example
of the space diversity, which uses a beamformer to incre-
ase the SINR for a particular direction [6-8].

M. D. MOGHADAM ET AL. 473
The first goal of this paper is to extend the works in [9]
and [10] by considering multiple-cell system and closed-
loop power control. In these works, a RAKE receiver in
single-cell system with conjugate gradient adaptive bea-
mforming was proposed in the presence of frequency-
selective Rayleigh fading channel, and perfect power
control (PPC) was considered.
In this work, the performance analysis of direct
sequence (DS)-CDMA system in frequency-selective
Rayleigh fading channel has been studied. If the delay
spread in a multipath channel is larger than a fraction of
a symbol, the delayed components will cause inter-symbol
interference (ISI). Adaptive receiver beamforming sch-
emes have been widely used to reduce both co-channel
interference (CCI) and ISI and to decrease the bit error
rate (BER) by adjusting the beam pattern such that the
effective SINR at the output of the beamformer is opti-
mally increased [11].
In this paper a RAKE receiver in DS-CDMA system is
analyzed in three stages according to Figure 1 [9]. In the
first stage, this receiver uses constrained least mean
squared (CLMS) adaptive beamforming algorithm to find
optimum antenna weights assuming perfect estimation of
the channel parameters (direction, delay, and power) for
the desired user. The desired user resolvable paths’
directions are fed to the beamformer to reduce the inter-path
interference (IPI) from other directions. Also, the RAKE
receiver uses conventional demodulation in the second
stage and conventional maximal ratio combining (MRC)
in the third stage to reduce multiple access interference
(MAI) and the other interferences. Reducing the MAI
and CCI will further decrease the system BER.
To improve the performance of cellular systems, base
station assignment (BSA) technique can be used. In the
joint power control and base station assignment, a num-
ber of base stations are potential receivers of a mobile
transmitter. Here, the objective is to determine the ass-
ignment of users to base stations which minimizes the
allocated mobile powers [12-15]. In simple mode and in
multiple-cell systems, the user is connected to the nearest
base station. This way is not optimal in cellular systems
under the shadowing and multipath fading channels and
can increase the system BER.
Accordingly, the second goal of this paper is to use
base station assignment technique. In [14], the combined
the base station assignment and power control was used
to increase uplink capacity in cellular communication
networks. In that work, it was shown that if there exists
at least one feasible base station assignment, the prop-
osed algorithm will find the jointly optimal base station
assignment and minimal transmitter power level for all
users. In this paper, we present the base station assignm-
ent method based on minimizing the transmitter power
(BSA-MTP) for decreasing the BER in all cells.
The organization of the remainder of this paper is as
follows. The system model is presented in Section 2. The
RAKE receiver structure is described in Section 3. In
Section 4, we propose smart step closed-loop power con-
trol (SSPC) algorithm. In Section 5, the BSA-MTP tech-
nique is presented. Section 6 describes switched-beam
(SB) technique and equal sectoring (ES) method. Finally,
simulation results and conclusions are given in Section 7
and Section 8, respectively.
2. System Model
In this paper, we focus on the uplink communication
paths in a DS-CDMA cellular system. replicas of the
signal, due to both some form of diversity reception (for
instance antenna diversity) and channel frequency selec-
tivity, are assumed Rayleigh distributed and optimally
combined through a RAKE receiver according to Figure 1.
L
Figure 1. Block diagram of a three-stage AKE receiver in DS-CDMA system [9]. R

474 M. D. MOGHADAM ET AL.
Also assume that there are
M
active base stations in
the network, with m
K
users connected to th base
station, where . Also assume that each base
station uses an antenna array of sensors and
weights, where , to receive signals from all users.
Also, for simplicity we assume a synchronous DS-
CDMA scheme and BPSK modulation in order to simp-
lify the analysis of proposed methods. Additionally, in
this paper we assume a slow fading channel. Hence, the
received signal in the base station q and sensor
m
1M
S
m
N
S N
s
from all users can be written as [9,16]
  
 

,, ,,,
1
,,, ,,
,
exp sin
L
qskmkkmlkmkml
kl
kmkmldkml
rtpxyb t
ctjsknt



  
 
,,
c
(1)
where is the pseudo noise (PN) chips of user
in cell (user ) with a chip period of ;
is the information bit sequence of user
with a bit period of where G is processing
gain;

,km
ct
m

t
,,kml
k
,
b
,km
b
T
c
T
,km

km
GT

is the lth path time delay for user ; ,km
,,kml

is the direction of arrival (DoA) in the th path
for user ;
l
,km ,,kml

is the complex Gaussian fading
channel coefficient from the lth path of user ; ,km
2/d
d
k

  where

is signal wavelength and d
is the distance between the antenna elements that for
avoid the spatial aliasing should be defined as 0.5d


and is an additive white Gaussian noise (AWGN)
process with a two-sided power spectral density (PSD) of
. Also for conventional BSA technique,

nt
0
N/2


,
k
x
y
is defined as
 


,
,
BS
/10
,
/10
,
1;
min, 10
,
;
,10
km
k
kq
q
L
km
km
o
L
kq
kS
dxy
xy
kS
dxy














(2)
where L

is path-loss exponent; and
are the distance between user and BSm
and BS, respectively (see Figure 2). Also the variable
defined the set of the nearest BSs to user ;

,, km
dxy
k
k


,, kq
dx
k


y
q
,km

is a random variable modeling the shadowing between
user and BS; is the set of users that con-
nected to BS and is the set of users that not con-
nected to BS [2]. Also in (1)
kmBSq S
o
Sq
q

,/10
,,
,10
km
L
km kmkm
pdxy p

 


,
(3)
is the received power in the BS of user in the m,km
Figure 2. The distance between two pairs of mobile trans-
mitters and base station receivers [12].
presence of closed-loop power control where is
the transmitted power of user that in the case of
the PPC,
,km
p
,km
,km
p

is fixed for all users within cell
(
m
,km
p/
b b
ET


where is the energy per bit for all
users) [2,9].
b
E
The received signal in the base station in sensor q
s
for user is given by [9] ,iq



 
,,,, ,,, ,,,,
1
,, ,,
exp sin
L
iqsiqiq iqliq iqliql
l
diqliqs
rtpbt ct
jskItn t






 
 (4)
where


,,iqs
I
t is the interference for user in sen-
sor
,iq
s
and can be shown to be
  
 
,,,,, ,
11 1
,,
,,,,, ,,
,
exp sin
m
K
ML
iqskm kkmkml
mk l
km iq
kmkmlkmldkml
Itp xybt
ct jsk


 



 
 
(5)
where m
K
is the number of users in cell and m
M
is the number of base stations/cells.
3. RAKE Receiver Performance Analysis
The RAKE receiver structure in the DS-CDMA system
is shown in Figure 1. The received signal is spatially
processed by a beamforming circuit with CLMS algorithm,
one for each resolvable path ( beamformers). The re-
sultant signal is then passed on to a set of parallel matched
filters (MFs), on a finger-by-finger basis. Also, the output
signals from the matched filters are combined ac-
cording to the conventional MRC principle and then are
fed into the decision circuit of the desired user [9].
L
L
C
opyright © 2010 SciRes. WSN

M. D. MOGHADAM ET AL. 475
3.1. Constrained LMS Algorithm
It is well known that an array of weights has N1N

degree of freedom for adaptive beamforming [9,16]. This
means that with an array of weights, one can gener-
ates pattern nulls and a beam maximum in des-
ired directions. From (5), it is clear that the number of
users is
N
1N
1
M
u
m
m
K
K


u
L
L
and the number of interferes is
. To null all of these interferes; one would have
to have weights, which is not practical. So, we
focus only on the paths of the desired user. Thus, the
minimum number of the antenna array weights is
where, typically, varies from 2 to 6 [9].
1
LK
u
LK
L
In this paper, we use the CLMS adaptive beamforming
algorithm. This algorithm is a gradient based algorithm
to minimize the total processor output power, based on
the look direction constraint. The adaptive algorithm is
designed to adapt efficiently in agreement with the envi-
ronment and able to permanently preserve the desired
frequency response in the look direction while minimiz-
ing the output power of the array. The combined form of
the constraint is called constraint for narrowband beam-
forming [12,17].
This form consider a narrowband beamformer where
the narrowband signal from each element of smart ant-
enna are multiplied by the complex weight calculated by
using narrowband adaptive beamforming algorithm, and
then summed to produce the output of the array. The
definition of the complex weights of this beamformer in
the th iteration for user in the th path is as
follows [16,18].
n,iq j
 
()() ()()
,,,0,,1,,1
... T
jjjj
iqiq iqiqN
nwnwnw n



w (6)
Accordingly, the output of the array in the th itera-
tion in the th path for user is given by
n
j,iq
 
() ()
,,,
H
jj
iqiq iq
ynn n

wr (7)
where .
,,,0,,1 ,,1
... T
iqiq iqiqN
rr r

  
 r
The expected output power of the array in the th it-
eration is given by
n


 

 

 
2*
()()()
,,,
() ()
,,, ,
() ()
,,
EE
E
jjj
iqiq iq
HH
j
iqiq iqiq
H
jj
iqrr iq
yn ynyn
nnn n
nn





wrrw
wRw

j
n
(8)
where is denoted the expectation and is the
correlation matrix of the received vector .

E.rr  R

n
,iq

r
A real-time CLMS algorithm for determining the op-
timal weight vector for user in the th path is
[17,18]:
,iq j
 


() ()()
,, ,
() ()
,,,,
1
1
jjj
iqiqiq
jHj
iqiqiqj
nng 

 




ww w
wa
(9)
where





()
,,, ,,
,,
[1 expsin...
...exp( 1)sin]
j
iqiq jdiqj
T
diq
jk
jk N




a
j

(10)
denotes spatial response of the array for user in the
th path. Also in (9), is the new weight
computed at the
,iq
j

()
,1
j
iq nw


1n

th iteration for user in the
th path. Also, the variable scalar
,iq
j

denotes a posi-
tive scalar (gradient step size) that controls the conver-
gence characteristic of the algorithm, that is, how fast and
how close the estimated weights approach the optimal
weights, and




()
,
j
iq
g
nw denotes an unbiased estimate of
the gradient of the power surface ()
which is the expected output power of the array) with
respect to
 
()
,,
H
jj
iq
nnwRw
()
iq rr



n
()
,
j
iq
w after the nth iteration. The algorithm
is “constrained” because the weight vector satisfies the
constraint at each iteration, that is


,, ,,
1
jH
iq j
()
iq
()j
iq


wa .
Rewrite the CLMS algorithm as follows [17].
 




()
,,,
()() ()()
,,, ,
1
j
iqiqj
jjj j
iqiq iqiq
nngn
N

  
a
wβww
(11)
where


() ()
,,, ,,,
()
,
H
jj
iqiq jiqiq j
j
iq N


aa
βI (12)
The gradient of with respect
to
 
() ()
,
H
j
iqrr iq
n

wRw
,
j
n


()
,
j
iq nw is given by [17]


 



()() ()
,,
()*
,
()
,
2
H
jj
iqiqrr iq
j
iq
j
rri q
,
j
g
nn
n






wwR
w
Rw
nw
(13)
and its computation using this expression requires
knowledge of rr


R, which normally is not available in
practice. For a standard LMS algorithm, an estimate of
the gradient at each iteration is made by replacing rr


R
by its noise sample

,,
11
H
iq iq
nn

rr available at
time instant


1n

, leading to




 
() ()*
,,,
21
jj
iqiq iq

g
nny

 wr n
(14)

476 M. D. MOGHADAM ET AL.
The CLMS is a fast convergence algorithm. However,
it is drastically sensitive to the mismatch in the direction
of arrival. Meanwhile, the weights estimated by the stan-
dard algorithm are sensitive to the signal power, require-
ing a lower step size in the presence of a strong signal for
the algorithm to converge, which in turn regarding the
decrease of mis-adjustment error, the convergence time
is increased [17,19].
It should be mentioned that for the antenna arrays
weight vector in the CLMS algorithm and for big

,
will converge after a few iteration (is approximately
equal to the number of beamformer weights, i.e., nN

)
[19].
Accordingly, the output signal from the th beam-
former () can be written as [9]
j
1,...,jL
 



 
()
,,, ,,, ,,,,
()
,,
j
iqiqiqiq jiqiq jiq j
jjj
iq iq
yt pbtct
ItItnt

 



(15)
where is a zero mean Gaussian noise of vari-
ance

()j
nt
2
n

and


,
j
iq
I
t
, the IPI, is defined as

 

()
,,,,,,,,
1
,,,
L
jj
iqiqiqiqliql iqiql
l
lj
iq iql
It pgbt
ct










,,
(16)
and


,
j
iq
I
t, the MAI, is defined as

  

()
,,
111
,,
,,, ,,, ,,
,
m
K
ML
jj
iqkm kiqkml
mk l
km iq
kmlkmkmlkmkml
Itp xyg
bt ct

 




 
 ,,,
(17)
where

()
,
j
iq
g

is the magnitude response of the th
beamformer for user toward the DoA
j
,iq

[9].
3.2. Matched Filter
Using beamforming in the first stage, will reduce the IPI
for the desired user and the MAI from the other users
whose signals arrive at different angles from the desired
user signal (out-beam interference). Now, in the second
stage of the RAKE receiver, the output signal from the
th beamformer is directly passes on to a filter matched
to the desired user’s signature sequence. The th matched
filter output corresponding to the th bit is [9]:
j
j
n
 
 
() ()
,,,,,,
() ()
,
j
iqiqiqiq jiq
jj
iq
znpbnI n
Innn





j
(18)
where




,,
,,
()
,,,
(1)
1biqj
biqj
nT
j
j
iqiqiqiq j
bn T
,,
I
nItct
T








dt (19)


 
,,
,,
()
,,,
(1)
1biqj
biqj
nT
j
j
iqiqiqiq j
bn T
,,
I
nItct
T






dt (20)
and
  
,,
,,
() ()
,,,
(1)
1biqj
biqj
nT
jj
iqiq j
bn T
nnntct d
T






t

(21)
If we assume that the paths’ delays from all users are
less than the symbol duration for all users’
signals on all paths, the th bit IPI and MAI at the out-
put of the th matched filter are expressed as [9]

,,  kmlb
T 
n
j
  

() ()
,,,,,,,
1
,,, ,,
L
jj
iqiqiqiqliql iq
l
lj
iiiq jiql
,
I
npg b
R








n
(22)
and
 

 
() ()
,,
111
,,
,, ,,,,,,
,
m
K
ML
jj
iqkm kiqkml
mkl
km iq
kmlkmikiqjkml
Inp xyg
bnR







 ,,,
(23)
where the autocorrelation function

,ik
R

is [9,20]:
  
,,,
1
b
ikiq km
bT
Rctct
T
 
dt (24)
If all users’ delays are multiples of the chip period
(), then
c
T
   
12
11
,,1,21
00
1GG
ikiq kmcc
ll
RclclRl
G




 2
lT(25)
where the autocorrelation function

c
R

is:
  
1
b
c
bT
Rctct
T
 
dt
 (26)
In the case of a maximal-length sequence (m-sequence)
and for 0b T


, we have [20]:
 
111/;
1/ ;
c
c
c
c
GT
T R
GT












(27)
3.3. Maximal Ratio Combining
Diversity combining has been considered as an efficient
way to combat multipath fading because the combined

M. D. MOGHADAM ET AL. 477
SINR is increased compared with the SINR of each div-
ersity branch. The optimum combiner is the MRC whose
SINR is the sum of the SINR’s of each individual diver-
sity branch [20,21].
minimized, but at the same time, the user SINRs satisfies
the system quality of service (QoS) requirements [23].
Depending on the location where the decision on how
to adjust the transmitted powers is made, the power con-
trol algorithm can be divided into two groups: central-
ized power control and distributed power control [1-6,
12]. In centralized power control, a network center can
simultaneously compute the optimal power levels for all
users. However, it requires measurement of all the link
gains and the communication overhead between a net-
work center and base stations. Thus, it is difficult to re-
alize in a large system [24]. Distributed power control,
on the other hand, uses only local information to deter-
mine transmitter power levels. It is much more scalable
than centralized power control. However, transmitter
power levels may not be optimal, resulting in perform-
ance degradation [25].
After the finger-matched filter, the fingers’ signals are
combined according to the MRC principle in the third
stage of the RAKE receiver. In this paper, we use the
conventional MRC that the signal of user in the
th path is combined using multiplying by the complex
conjugate of
,iq
j
,,iq j

.
The SINR in output of the RAKE receiver for user
is [9,21]:
,iq
 
1
SINR SINR
L(j)
i,q i,q
j



 (28)
where
The distributed closed-loop power control problem has
been investigated by many researchers from many per-
spectives during recent years [4,23,26]. For instance, the
conventional fast closed-loop power control strategy
used in practice in CDMA systems is a fixed step con-
troller based on SINR measurements. The fixed step
closed-loop power control (FSPC) algorithm is defined
by [4]

  
2
,,,
22
() () ()
,,
SINR
EEE
iqiq j
(j)
i,q jj
iq iq
p
IIn





2
j
(29)
is the SINR in output of the RAKE receiver in path
for user .
j
,iq
Also, we can be rewritten the SINR in (29) by (30),
that shown at the bottom of the page, where


,
k
x
y
 
E,
k
x
y and


2
2
,, ,,
E
kmj kmj

  [9,22].

1*
,, ,,
sign
nn n
iqiqiq iq
pp




 (33)
where ,
n
iq
p

, *
,iq

, and ,
n
iq


are the transmitter power,
SINR target, and measured SINR of user at time ,iq
n

, respectively, and

is the fixed step size. Also
1
,
n
iq
p


is transmitter power control (TPC) command in
the feedback link of the base station to user at time ,iq
1n


(all signals are in decibels).
In order to perform the BER, we assume Gaussian ap-
proximation for the probability density function of inter-
ference plus noise. The conditional BER for a BPSK
modulation is [9,20]:
 

BER2 SINR
i,q i,q
Q

  (31)
Also, the distributed traditional closed-loop power
control (DTPC) is defined by [23] the variance of
where


2
1exp/ 2
2x
Qxu du



 (32) *
,
1
,
,
iq
n
iq iq
n
iq
p


,
n
p




 (34)
4. Smart Step Closed-Loop Power Control
Algorithm In both algorithms, the simple intuition behind this it-
eration is that if the current SINR ,
n
iq

 of user is
less than the target SINR
,iq
*
,iq

, then the power of that
user is increased; otherwise, it is decreased. It should be
mentioned that convergence speed of DTPC algorithm is
higher than FSPC algorithm. Also, the SINR mis-
A major limiting factor for the satisfactory performance
of CDMA systems is the near-far effect. Power control is
an intelligent way of adjusting the transmitted powers in
cellular systems so that the total transmit power (TTP) is

 

 
2
,,,
22
2()22 ()20
,,,,,,,,,,,,,,,,,,,,, ,
1111
,,lj kmiq 
SINR
,2
m
(j)
i,q
iqiqj
K
LML
j j
iqiq jiqiqliiiqjiqlkmkkm jiqkmlikiqjkml
lmkl b
p
N
pg RpxygRT







 

(30)

478 M. D. MOGHADAM ET AL.
adjustment in FSPC algorithm is higher than DTPC al-
gorithm. But, it has been shown that the FSPC algorithm
converge to a bound region *
,,
2
n
iq iqld
k



 , where
is the loop delay [4].
ld
k
Also in [26], variable step closed-loop power control
(VSPC) algorithm has been proposed. In this algorithm,
variable step size is discrete with mode . It is shown
that the performance of VSPC algorithm with mode
is found to be worse than that of a fixed step
algorithm () under practical situations with loop
delay of two power control intervals, but the conver-
gence speed of VSPC algorithm is higher than FSPC
algorithm. Also in this algorithm, the variance of the
SINR mis-adjustment is reduced in compared to FSPC
algorithm.
v
q
4
v
q
1
v
q
Practical implementations of power control in CDMA
systems utilize closed-loop control, where the transmitter
adjusts its power based on commands received from the
receiver in a feedback channel. To minimize signaling
overhead, typically one bit is used for the power control
command. In practice, the command must be derived
based on measurements made at the receiver, transmitted
over the feedback channel to the transmitter, and finally
processed and applied at the transmitter. All these opera-
tions constitute a loop delay, which can cause problems
if it is not properly taken care of in the design of the
power control algorithm. In many cases the loop delay is
known due to a specific frame structure inherent in the
system. A typical loop delay situation encountered in
WCDMA systems is shown in Figure 3. The slot at time
is transmitted using power . The receiver meas-
ures the SINR
nt
n
p
n

 over a number of pilot and/or data
symbols and derives a TPC command. The command is
transmitted to the transmitter in the feedback link and the
transmitter adjusts its power at time according
to the command. It should be mentioned that since the
power control signaling is standardized, the loop delays
are known exactly [4].

1n
t
In this paper, we propose the smart step closed-loop
power control algorithm. The SSPC algorithm defines as
follows.

1* *
, ,,,,,
sign
nn nn
iqiqiq iqiqiq
pp

  

   
(35)
The SSPC algorithm is implemented as follows.
1) Select the initial transmitted power vector (0n


)
for all users within cell as m
0000
1, 2,,
... m
mmmKm
pp p





p, . 1, 2,...,mM
2) Estimate the weight vector for all users with the
CLMS algorithm using (11).
3) Calculate the SINR for all users using (28).
4) If *
,,
n
km km0





 for each user then set
1
nn




1n
and calculate the TPC for all users at time


using (35) and go back to 2), where 0

is thre-
shold value.
Finally, if *
,,
n
km km0





 for all users then algo-
rithm ends.
As will be seen from simulation results, because of
variable coefficient in the sign function, the convergence
speed of our algorithm is higher than VSPC and FSPC
algorithms.
5. BSA-MTP Technique
The system capacity might be improved, if the users are
Figure 3. Example of power co
ntrol timing in WCDMA systems [4].
C
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M. D. MOGHADAM ET AL. 479
allowed to switch to alternative base stations, especially
when there are congested areas in the network. Obvi-
ously, when uplink performance is of concern, the
switching should happen based on the total interferences
seen by the base stations [15].
So far, we have considered the power control problem
for a number of transmitter-receiver pairs with fixed as-
signments, which can be used in uplink or downlink in
mobile communication systems. In an uplink scenario
where base stations are equipped with antenna arrays, the
problem of joint power control and beamforming, as well
as base station assignment, naturally arises [12].
In this paper, we modify the BSA-MTP technique to
support base station assignment as well. The modified
technique can be summarized as follows.
1) Initially by the conventional BSA technique, each
mobile connects to its base station, according to (2).
2) Estimate the weight vector for all users with the
CLMS algorithm.
3) Each mobile updates its transmitted power based on
the SSPC algorithm using (35).
4) Finally, /
ru
K
KM
 
users that their transm-
itted power is higher than the other users to be transfe-
rred to other base stations according to the following
equation, where the function
x


returns the integer
portion of a number
x
.







,
,
,
,
BS
/10
,
/10 BS
,
/10
,
/10
,
1;
min,10
,
,10
min, 10
;
,10
km
k
kq
km
k
kq
q
L
km
m
mq
kq
L
kq
L
km
m
o
L
kq
kS
dxy
;
x
yk
dxy
dxy
kS
dxy

















 







S (36)
where BSq S is the set of users that are in cell but not
connected to BS [2].
q
q
It should be mentioned that the technique for users that
are present in the border of cells, the BER can be effect-
tively reduced.
6. Switched-Beam Technique and Equal
Sectoring Method
One simple alternative to the fully adaptive antenna is
the switched-beam architecture in which the best beam is
chosen from a number of fixed steered beams. Switched-
beam systems are technologically the simplest and can
be implemented by using a number of fixed, independent,
or directional antennas [27]. We list the SB technique
conditions for this paper as follows.
1) Coverage angle for all beams is and overlap
between consecutive beams is . Thus each base sta-
tion has 36 beams.
30
20
2) Each user can be use one beam for its each path to
communicate with a base station at any time
Also, one of simple methods to sectorize a cell is equal
sectoring, in which all sectors have the same coverage
angle. In this paper, we assume three sectors for each
base station with sector angle for the ES method. 120
7. Simulation Results
We consider 4
M

base stations for a four-cell
CDMA system on a 22

grid as Figure 4. We assume
a uniform linear array of omni-directional antennas
in each base station with antenna spacing
S
/2 d


.
Also, we assume BPSK m-sequence code spreading with
processing gain 64G

; resolution ; the input
data rate
1R
9.6 kbps
b
T

; the number of antenna weights
3N

; the number of antenna sensors ; threshold
value
3S
00.1dB


; frequency-selective fading channel
with 2L

resolvable propagation paths; variance of
the complex Gaussian fading channel coefficient 2


4dB

; fixed step size for SSPC, FSPC, and VSPC
algorithms 0.01


; mode for VSPC algo-
rithm [26]; variance of the log-normal shadow fading
; path-loss component ; initial value
for weight vectors in the CLMS algorithm
4
v
q
L

2

 8dB 4


0

w0
0
m
;
initial value for transmitted power vectors

p0. The
SINR target value is the same for all users and is set to


dB
*
 57 . It also is assumed that the distribution of
users in all cells is uniform.
First, in order to compare the BSA-MTP and conven-
tional BSA techniques, we assume the PPC, and the BER
Figure 4. Location plot of base stations and users in four
cells.
C
opyright © 2010 SciRes. WSN

480 M. D. MOGHADAM ET AL.
has been calculated from (31). Finally, we compare the
TTP with the joint SSPC algorithm and BSA-MTP tech-
nique in comparison with other methods.
Figure 5 shows the average BER versus the sig-
nal-to-noise ratio (SNR) for different receivers (one,
two, and three-stage receivers) in the case of 32
u
K

active users and the PPC case. It should be mentioned
that in this simulation, users can to be trans-
ferred to other base stations with the BSA-MTP tech-
nique. Also in this simulation we use CLMS algorithm
or SB technique in the first stage of the RAKE receiver.
It is clear that, in MF only receiver (one-stage receiver)
and in the case of the conventional BSA technique, we
still have the error floor at high SNR. Using CLMS and
MRC receiver (two-stage RAKE receiver) or CLMS,
MF, and MRC receiver (the three-stage RAKE receiver
as Figure 1) has a better performance than using MF
only. Also observe that using the BAS-MTP technique
in the case of three-stage RAKE receiver (CLMS me-
thod in the first stage), the average BER is lower than
the conventional BSA technique. For example, at a
SNR of 20 dB, the average BER is 0.0096 for the
three-stage RAKE receiver with the conventional BSA
technique, while for the BSA-MTP technique, the av-
erage BER is 0.0031. Also it can be seen that the aver-
age BER in the SB technique is less than the CLMS
algorithm. Also, it is clear that the MAI is not removed
totally and the performance is still worse than the single
user per cell bound.
8
r
K
Figure 6 shows the average BER versus the number of
active users (u
K
) for different receivers as Figure 5, in
the case of the PPC and . At a BER of
0.005, the three-stage RAKE receiver (CLMS method in
the first stage) with the BSA-MTP technique support
users, while for the three-stage RAKE receiver
and the conventional BSA technique support
SNR10 dB
29
u
K
18
u
K

users. We also observe that the three-stage RAKE re-
ceiver can achieve lower BER than the one and two-
stage receivers. Also at a BER of 0.002, the three-stage
RAKE receiver for the SB technique in the first stage
and for the BSA-MTP technique support users,
while the CLMS, MF, and MRC receiver support
users. It should be mentioned that increasing
the number of active users in the SB technique, will lead
more complexity in receiver in comparison with the
CLMS algorithm. Also increasing the number of active
users (
49
u
K
18
u
K
u
K
), will increase the number of users that can to
be transferred to other base stations (r
K
) in the BSA-
MTP technique.
Figure 7 shows the comparison of the average SINR
achieved over users versus the power control
iteration index (n) for SSPC, VSPC, and FSPC algo-
rithms and for BSA-MTP and conventional BSA tech
32
u
K
Figure 5. Average BER of all users versus the SNR for the
PPC case.
Figure 6. Average BER for all users versus the number of
active users for the PPC case and SNR = 10 dB.
niques. In this simulation, the three-stage RAKE receiver
uses CLMS, SB, or ES methods in the first stage. Also,
we assume that each user to have a maximum power
constraint of 1 watt. Accordingly, we observe that the
convergence speed of the SSPC algorithm is faster than
the VSPC and FSPC algorithms. The figure also shows
that the SSPC algorithm with the BSA-MTP technique
converges faster than the SSPC algorithm for the con-
ventional BSA technique. In addition, we see that the
convergence speed of the SSPC algorithm for the SB
technique is faster than the CLMS and ES methods. Also
observe that the average SINR level achieved is below
the target SINR value for the ES method, because in this
method, the MAI is much higher than SB technique and
CLMS algorithm.
Figure 8 shows the comparison of TTP usage versus
the power control iteration index () when there are n
32
u
K

users in all cells according to Figure 7. But in
this simulation, we assume that users have no maximum
power constraints. Similar to Figure 7, we observe that
the ES method never can achieve the target SINR value.
Also this figure shows that the SSPC algorithm offers
more savings in the TTP as compared to the VSPC and
FSPC algorithms. In addition, the figure shows that the

M. D. MOGHADAM ET AL. 481
Figure 7. Average SINR of all users versus power control
iteration index with maximum power constraint of 1 watt.
Figure 8. Total transmit power of all users versus power
control iteration index. No power constraints.
TTP in BSA-MTP technique is less than conventional
BSA technique. Also it can be seen that the TTP for the
SB technique is lower than the CLMS algorithm, because
in SB technique, the MAI is lower than CLMS algo-
rithm.
8. Conclusions
In this paper, we studied the RAKE receiver performance
of multiple-cell DS-CDMA system with the space diver-
sity processing, Rayleigh frequency-selective channel mo-
del, closed-loop power control, and base station assi-
gnment. This receiver consists of CLMS, MF, and MRC
in three stages.
Accordingly, we proposed the SSPC algorithm and the
BSA-MTP technique to reduce the CCI and the MAI. It
has been shown that, by using antenna arrays at the base
stations, the SSPC algorithm and the BSA-MTP tech-
nique will decrease the interference in all cells. In addi-
tion, it can be seen that the TTP in the SSPC algorithm is
less than the VSPC and FSPC algorithms. Also our res-
ults show that the TTP for BSA-MTP technique is lower
than conventional case. Thus, it decreases the BER by
allowing the SINR targets for the users to be higher, or
by increasing the number of users supportable at a fixed
SINR target level. On the other hand, it has been shown
that the convergence speed of the SSPC algorithm is inc-
reased in comparison with the VSPC and FSPC algo-
rithms. It has also observed that using the BSA-MTP
technique will decrease the average BER of the system to
support a significantly larger number of users.
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