A Cell Planning Scheme for WCDMA Systems Using Genetic
Algorithm and Measured Background Noise Floor
Hsin-Piao Lin1, Rong-Terng Juang1, Ding-Bing Lin1, Cheng-Yi Ke2 and Yi Wang2
1Institute of Computer, Communication and Control, National Taipei University of Technology.
No. 1, Sec. 3, Chung-Hsiao E. Road, Taipei, Taiwan.
[email protected], [email protected], [email protected]
2 Taiwan Cellular Co., 4Fl. -2, No. 10, Lane 609, Sec. 5, Chung-Shin Rd., San-Chung, Taipei,
Taiwan.
Abstract–WCDMA is an interference-limited system with coverage and data
throughput sensitive to background noise. This paper presents the background
noise measurements in urban Taipei city for the licenses bands of 3G systems
issued in Taiwan. The measurements involve with FDD mode uplink and downlink
frequency bands measured on building tops and at street level, respectively. The
severeness of spectrum pollution of these bands is evaluated by extracting three
statistic parameters from the measurements, and the impact of the background
noise on coverage and throughput is analyzed for the WCDMA systems. Besides,
basing on measurements, a better solution using a genetic algorithm with the help
of propagation model and digitized building information is proposed for the cell
planning of WCDMA systems, by which the required coverage can be met with the
optimum solution for BS number, locations, antennas heights, and transmitting
power, so as to obtain a system suffering from less impact of the background noise
and achieve higher data throughput with minimum cost.
Keywords – WCDMA, background noise, cell planning, genetic algorithm.
1. INTRODUCTION
Third generation (3G) mobile communication systems brought up many attentions in
these few years. Many governments have released licensed frequency bands for 3G
services. Among the proposed radio transmission technologies, WCDMA (Wideband
code division multiple access) technology has emerged as the most widely adopted 3G
air interface. CDMA is characterized as an interference-limited system, i.e. an increase
of the interference level determines a decrease of the system performance. The
performance degradation due to interference from adjacent narrow band systems has
been evaluated for a WCDMA network [1][2], where the capacity per cell is sensitive to
the coverage and interference.
In Taiwan, the 3G bands are divided into five licenses. Due to the tradeoff between
the cost and performance, system operators need to evaluate the severeness of the
spectrum pollution of those bands before deploying the networks. A direct solution is
conducting the measurements for the background noise floor and utilizing statistic
parameters to process the large number of measurements. Meanwhile, these
measurement data could be used for cell planning in the initial stage of system
development.
Cell planning, the key the system performance and economical efficiency, is a
complex and important issue in cellular communication systems. For the degrading of
system performance due to interference in WCDMA networks, cell planning could not
base only on signal prediction but must also consider the power limits and the signal
quality constraints. A mathematical programming model, proposed in [3], considers the
Signal-to-Interference Ratio (SIR) to support the decisions on where to locate the new
base stations (BS) and which configuration to be selected for each of them.
Numerous attempts have been proposed to optimize network performances in terms
of capacity, coverage, quality of service, etc. A tuba search approach for cell planning
with capacity expansion was proposed in [4], which considers two types of BSs: some
existing ones in service and some additional BSs needed to be determine for the
increased traffic demand. The influence of site location and antenna tilts onto the
operation of UMTS systems were presented in [5]. A genetic algorithm (GA) based
automatic cell planner was proposed in [6], which adjusts antenna parameters and BS
transmitted power to improves the network performance. The optimizing of BS
locations as well as their configurations was addressed in [7], which utilizes a
mathematical programming model considering the power control mechanism and the
SIR as quality measures and employs a tabu algorithm to find the approximate solutions
of the problem.
However, little attention has given to the impact on background noise on system
performance. This paper proposes a methodology for the initial development of
WCDMA systems to mitigate the influence of the background noise and increase the
throughput. A GA with the help of Walfisch-Ikegami propagation model, verified as an
accurate model for predicting propagation path loss in urban area with smaller cells [8],
and digitized building information is used to achieve the optimization of cell planning.
GA, developed by Holland [9], is a nature-inspired algorithmic technique basing on the
principles of natural evolution and widely used to solve optimi zation problems [10][11].
In the proposed method, the required coverage can be met with the optimum solution
for BS numbers, locations, and antennas heights.
This paper is organized as follows. Section 2 presents the measurements of
background noise level in urban Taipei city. Statistical parameters are used to evaluate
the spectrum qualities of the 3G license bands. The performa nce degradation due to the
background noise for the WCDMA system is also studied here. Section 3 addresses the
cell planning methodology based on the background noise measurements. Section IV
takes an example for deploying a WCDMA network in a real environment. In Section V,
conclusions are drawn.
2. BACKGROUND NOISE FLOOR MEASUREMENTS IN URBAN TAIPEI
CITY
Table 1 summarizes the frequency bands of 3G services issued in Taiwan. Except
license E, each one has a FDD (frequency division duplex) mode uplink frequency band,
a FDD mode downlink band, and a TDD (time division duplex) mode frequency band.
During the summer of 2001, we carried out lots of background noise measurements
involving with FDD mode uplink and downlink bands in urban Taipei city. The
measurements for uplink and downlink bands were conducted on building tops and at
street levels, respectively. A drive test solution for WCDMA, Agilent E7476A, was use
to complete the measurements for the downlink frequency bands, and a spectrum
analyzer, ADVENTEST U3641, was used to measure the noise for the uplink bands at
four directions, east, west, south, and north at each selected location. Licenses A, B, C
and D were measured for both uplink and downlink bands, and the frequencies, power
levels and GPS coordinates were recorded in a notebook during the measurements.
Three statistic parameters are used to process the large number of measurements for
the evaluations of the spectrum clearness. The first parameter is the cumulative
distribution function (CDF) of noise power levels, which reveals the percentage of noise
levels under a certain power thresholds. The second one is the statistics of frequency
domain power level crossing rate (FD-LCR), the rate that the noise power envelope
crosses certain specified power thresholds in a positive-going direction. For simplicity,
this quantity is taken as the average of the rates obtained from the measurement at the
four directions. However, CDF and FD-LCR are not sufficient for spectrum quality
evaluation because the noise bandwidth is a more important dominator for
communication quality in CDMA systems. Hence, the third parameter is the average
noise bandwidth, in which the noise power levels are above certain specified power
thresholds. Considering the FD-LCR and average noise bandwidth together, by which
gives the noise bandwidth and crossing times per unit bandwidth at different power
thresholds, it is easily to distinguish the relative spectrum qualities between the bands.
Figure 1, 2, and 3 are the statistical information of the license bands measured at the
same location, where (a)s are that for the FDD mode uplink bands and (b)s for the
downlink bands. Figure 1 shows the CDFs of the background noise power levels, and
the severeness of spectrum pollution in ascending order is license D, C, B then A in
terms of the CDFs. Figure 2 exhibits the average FD-LCRs of the background noise
envelopes measured at the four directions, and the severeness in ascending order is also
license D, C, B then A in terms of the FD-LCRs. Figure 3 displays the average
bandwidth of the background noise. Though a slight ambiguity appears in the
distinguishing of pollution severeness, it is confident to conclude the spectrum quality
in descending order as license D, C, B then A.
Having estimated the spectrum qualities, the discussion goes on to the impact of the
background noise levels on cell coverage and throughput for WCDMA systems. The
analysis begins by contemplating the definition of the required signal to noise ratio
(SNR). The uplink case is explained in the first place and the SNR of user j is given by
[12]
)(
)(
cjNF
Rxj
jjj IP
P
RvW
SNR+
⋅= (1)
where W is the chip rate, 3.84Mcps, jv is the activity factor at physical layer, 0.67 for
speech and 1.0 for data, jR is the bit rate, )(RxjP is the received power from user j,
NFP is the background noise floor including thermal noise and noise from any
man-made transmitters within other communication systems, and )(cjI , the co-channel
interference associated with user j, is defined as the interference power coming from
other links operating at the same frequency band within the same system. The total
received power totalI can be expressed as the summation of the background noise floor,
co-channel interference, and the received power from user j, i.e.
)()( Rxj
cjNFtotal PIPI ++= . Defining totalj
Rxj ILP ⋅=)( , the load factor jL of one link has
the form
jjjb
j
vRNEW
L
⋅⋅+
=
)/(1
1
0
(2)
where jb NE )/( 0 is the SNR of link j. For N users with the same traffic type in the cell,
the system loading η is
∑=
=N
jjL
1
η (3)
The received power excluding the background noise floor can be given by
totalNFtotal IPI ⋅=− η (4)
It is more precise to consider each single service (12.2kpbs, 144kpbs, etc.) in system
dimensioning at each time. However, for succinctness, a compound traffic pattern is set
as a mixed of 80% speech users (12.2kpbs user data rate), 15% of 144kpbs and 5% of
384kpbs data users in the performance analyses below. The minimum SNR
requirements for the traffic pattern are assumed as 5dB, 1.5dB and 1.0dB, respectively.
According to (2), the averaged load factor, jL , of one link with respect to the specified
traffic pattern is 0.0185 if the SNR of every link reaches the minimal signal, which
leads to the acceptable BER (bit error rate) performance. In the calculation of uplink
coverage, the total received power, totalI , can be obtained using (4) if the system
loading and the background noise floor are given. Afterward the minimum received
power from user j is determined by totaljRx
j ILP ⋅=)( . Given the mobile transmitting
power as 0.5W, the maximum allowed path loss can be evaluated because )(RxjP is the
product of the mobile transmitting power multiplying the path gain, the path loss
expressed in decibel. Fig. 4 shows the relationship between the background noise floor
and the maximum allowed uplink path loss under different system loadings, 50%, 60%,
and 70%. The figure indicates that a lower noise level determines a larger cell coverage
range.
The analysis of the downlink throughput is based on a similar principle as the uplink
case. The BS transmitting power is 1W, the compound traffic pattern is adopted, and 4
different maximum allowed path losses, 130dB, 135dB, 140dB and 145dB, are set. Here,
the SNR of each link is also asked to meet the minimum requirement. Accordingly, the
total received power totalI is solved by totaljRx
j ILP ⋅=)( . Once the background noise
floor is given, the system loading η can be obtained according to (4). Consequently,
the system throughput is yield by the product of the average data rate multiplying the
system loading. Figure 5 shows the relationship between the background noise floor and
maximum downlink throughput. This figure exhibits that the throughput is higher at a
lower noise level.
Table 2 summarizes an example of the system performance evaluation from the
measurements for the uplink and downlink bands of license A, B, C and D. The
transmitting powers of the BS and mobile are set as 1W and 0.5W, respectively, and the
traffic pattern is the same as previous studies. The table shows that license D is the band
with the lowest mean noise power and maximum cell range for uplink, and license A
suffers from heavier impact of background noise. For downlink, license D is the one
with lower mean noise power, and license C suffers from heavier impact of background
noise.
3. CELL PLANNING USING GENETIC ALGORITHM
The proposed cell planning scheme focuses on the mitigation of performance impact
from background noise and efficiently operating of the WCDMA networks. Basing on
the background noise measurement, a simple GA with the help of Walfisch-Ikegami
model and digitized building information is proposed to achieve the optimization of cell
planning for the WCDMA system. The detailed description is given below.
A. Simple Genetic Algorithm
GA is a nature-inspired algorithmic technique for optimization problems based on the
principles of natural evolution. The individuals with better gene, leading to be fitter for
the environment, survive in the evolution process, but otherwise eliminated. After the
elimination, the survivals mate with each other and bear their offspring. The offspring
inherit their parents’ genes, which are the same as their parents or even better.
Consequently, the best gene could be obtained by iterating the evolution process.
The GA used here is a binary version and much likely as in [13]. The following gives
a briefly description of the main components used in binary GA;
1) Chromosome Encoding: The GA begins by defining a chromosome, which is
encoded as a binary string according to the characteristics of each individual.
2) Fitness Function: It is used to evaluate the fitness of every individual in the
environment. The user must decide which parameters of the problem are most
important because too many parameters bog down the GA.
3) Selection: It occurs at each generations or iteration of the algorithm. The individuals
with higher fitness will survive for mating, but otherwise will be discarded to make
room for the new offspring.
4) Crossover: It is the creation of offspring by recombining the chromosomes of
selected parents. Basic crossover methods include One-point crossover, multi-point
crossover, and uniform crossover [11].
5) Mutation: It introduces traits not in the original population. For binary operation, the
mutation is implemented by changing 1s to 0s or 0s to 1s on certain randomly
selected points in chromosomes.
Basing on the standard operations as selection, crossover and mutation, GA can solve
optimization problem easily.
B. Cell Planning Using Simple Genetic Algorithm
Figure 6 is the flowchart of the proposed cell planning. The possible locations for
setting BSs are selected according to the digitized building information, and then the
performance of each BS is evaluated basing on the principles in section 2. The
maximum uplink allowed path loss is determined under the conditions 1) 0.5W mobile
transmitting power, 2) 75% system loading, and 3) the presence of uplink background
noise in the cell. The background noise floor used here is the average of measurements
at street level for downlink case and on building tops for uplink case within a radius of
300m. The cell range is calculated by Walfisch-Ikegami model [14][15], which is a
hybrid model combining with diffraction down to street level and some empirical
correction factors. The throughput is evaluated under the conditions 1) specified
transmitting power, 2) the cell’s maximum uplink allowed path loss, and 3) the presence
of downlink background noise in the cell. Accordingly, the possible BSs’ locations
accompanied by their cell performance are obtained, and the next step goes to utilize the
simple GA to decide what combination of these possible BSs is the best solution to cell
planning. The decision is based on the fitness of each chromosome using the fitness
function, which considers the efficiencies of coverage and transmission and designed
for chromosome i as
iTi
Ti
ref
avgi
ii BCCCC
TT
CF1)0,max(
2)(
⋅
−−
⋅+= (5)
where iC is the percentage of the covered area, )(avgT is the average data throughput,
avgP is the average transmitting power of used BSs, refT is the throughputs with
respect to the measured minimum noise power level, TC is the desired coverage, and
B is the number of used BSs. The survivals are selected for mating in Selection and
generate offspring in Crossover. The last operation is mutation for introducing the traits
not in the population. The iteration stops to output the solution if the algorithm
continues without improvement, i.e., the algorithm continues the same best
chromosome for a certain iterations.
4. AN EXAMPLE OF DEPLOYING WCDMA CELLS
The selected area for simulation and validation is in the vicinity of NTUT (National
Taipei University of Technology) campus as shown in Fig. 7, which is an area of 2.5kms
by 1.6kms square digitized building map. Those blocks with brighter color represent
higher buildings and darker ones represent lower buildings. The average building height
and the standard deviation are 18.2m and 13.6m, respectively. Four BS transmitting
powers, 1W, 1.5W, 2W and 2.5W, are available, and the mobile transmission power of
0.5W and the same compound traffic pattern are set. The first step of the planning is the
selection of the possible locations for setting BSs according to the digitized building
information. One of the selection criterions is that the BS antenna height must higher
than average building height and lower than 43m. The second step is the evaluation of
each BS’s cell performance impacted by the measurement background noise. Finally,
the characteristics of these possible BSs are encoded as chromosomes and started
evolution by the GA. Here, uniform crossover method is used, and the desired coverage,
the survival rate, and the mutation probability are set as 90%, 50%, and 0.15,
respectively. Each generation composes of 200 chromosomes, and the iteration is
stopped to output the solution when the algorithm continues with the same best
chromosome for 50 iterations. Figure 7 shows the simulation results, where there BSs
with omni-directional antennas (⊕), as marked as BS1, 2 and 3, are needed for serving
this area. Their transmitting powers are 2.5 W, 2.0 W and 1.0 W, respectively, and
antenna heights are the same as 43m. The dotted points are the area covered by the
designed WCDMA system and the coverage rate is 92.6% with total throughput as 7.52
Mbps.
5. CONCLUSIONS
The coverage and throughput of 3G systems are sensitive to noise power level. This
paper has presented the results of noise power measurements in urban Taipei city for the
3G license bands issued in Taiwan. The severeness of the spectrum pollution of these
bands is evaluated by extracting some statistic parameters from the measurements. The
impact of background noise on coverage and throughput for WCDMA systems has been
analyzed, and the spectrum qualities for uplink and downlink bands of license A, B, C
and D have been evaluated. Also, basing on the noise measurements, a cell planning
scheme is developed using a simple GA with the help of Walfisch-Ikegami model and
digitized building information. A selected example shows that the proposed method can
reduce the impact of background noise and use minimum BSs to achieve the maximum
coverage and throughput. Thus, an easy and efficient cell planning for WCDMA
systems in the initial stage of system development can be delivered, and the deployed
system would suffer from less impact of background noise power and achieve
maximum performance with minimum cost. For an extended discussion, the
considerations should include traffic distribution, interference prevention, cell
sectorization, etc, when the amount of the subscribers increases. The growth of traffic
yields an increase of interference and a complicated cell planning. The cost function for
system optimization should be elaborately designed due to the multifarious
interconnected criteria. The discussions of the issues as a whole are taken as the future
work.
ACKNOWLEDGMENTS
This research is sponsored by Taiwan Cellular Co., Taiwan, under Contract
PSCF-91-006.
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Table 1
The Spectrum Distribution of The 3G Frequency Bands Issued in Taiwan
FDD mode
uplink downlink TDD mode
License A 1920~1935 MHz 2110~2125 MHz 1915~1920 MHz
License B 1935~1945 MHz 2125~2135 MHz 2010~2015 MHz
License C 1945~1960 MHz 2135~2150 MHz 2015~2020 MHz
License D 1960~1975 MHz 2150~2165 MHz 2020~2025 MHz
License E 825~845 MHz 870~890 MHz
Table 2
An Example of Performance Evaluation of the 3G License Bands Issued in Taiwan
License A License B License C License D FDD
UL FDD DL
FDD UL
FDD DL
FDD UL
FDD DL
FDD UL
FDD DL
50% system loading
149.4 151.8 150.1 151.5 150.7 150.9 150.9 152.5
60% system loading
148.4 150.8 149.1 150.5 149.7 149.9 149.9 151.5
Max. Allowed
Path Loss (dB) 70% system
loading 147.1 149.5 147.8 149.2 148.4 148.6 148.6 150.2
Lmax=130 2.72 2.73 2.72 2.73 2.72 2.72 2.72 2.73
Lmax=134 2.71 2.72 2.71 2.71 2.71 2.71 2.71 2.72
Lmax=140 2.65 2.69 2.66 2.68 2.67 2.67 2.67 2.69
Max. Throughputs
(Mbps)
Lmax=145 2.48 2.59 2.52 2.58 2.55 2.55 2.55 2.61
UL:Uplink DL:Downlink
Fig. 1. CDFs of the background noise power levels. a) Uplink frequency bands and b)
downlink frequency bands.
Fig. 2. FD-LCRs of the background noise envelopes at different power thresholds. a)
Uplink frequency bands and b) downlink frequency bands.
Fig. 3. Average bandwidth of the background noise at different power thresholds. a)
Uplink frequency bands and b) downlink frequency bands.
Fig. 4. Maximum allowed path loss at different background noise power floors for
different system loadings in the uplink WCDMA system.
Fig. 5. Maximum data throughput at different background noise power levels for
different path loss (Lmax) in the downlink WCDMA system.