Background Noise Floor Measurements and Cells Planning for WCDMA System

Hsin-Piao Lin1, Rong-Terng Juang1, Ding-Bing Lin1, Cheng-Yi Ko2, Yi Wang2

1Institute of Computer, Communication and Control, National Taipei University of Technology. No. 1, Sec. 3, Chung-Hsiao E. Road Taipei, Taiwan, +886-2-27712171 ext. 2271.

hplin@en.ntut.edu.tw, s9418009@ntut.edu.tw, dblin@en.ntut.edu.tw,

2 Taiwan Cellular Co., 4Fl.-2, No. 10, Lane 609, Sec. 5, Chung-Shin Rd., San-Chung, Taipei, Taiwan. Bill_Ko@tcc.net.tw, yiwang@tcc.net.tw

Abstract–The third generation mobile communication systems brought up many attentions in this few years. WCDMA system is an interference limited system and the coverage and data throughput are sensitive to background noise floor. In this paper, we present the background noise floor measurements in urban Taipei city. The FDD mode uplink and downlink frequency bands of 3G licenses issued in Taiwan are measured on building top and at street level respectively. We compile the statistics of the measurement data and analyze the impact of background noise power levels on coverage and data throughputs for WCDMA system. Besides, based on the noise power level measurements we proposed a better solution for the cells planning of WCDMA system. In this paper, we used simple genetic algorithm with the help of accurate prediction model and digitized building information to achieve the optimization of cell planning for WCDMA system. In our proposed method, the required coverage can be achieved with the optimum solution for base station numbers, locations, antennas heights, and transmitting power. Thus, the system would suffer from less impact of background noise power and achieve maximum performance with minimum cost. Keywords – WCDMA, Cells planning, Genetic Algorithm.

I. INTRODICTION

In Taiwan, the third generation mobile communication services frequency bands are divided into five licenses and each one has FDD mode uplink frequency band, FDD mode downlink frequency band and TDD mode frequency band. Because WCDMA system is interference-limited system, the system performance such as coverage and capacity are impacted by interference directly. Due to the problem of cost and system performance, it is necessary to evaluate the quality of those licenses before investment for a system operator. A direct solution is carrying out background noise floor measurements and then analyzing the measurement data. In order to process large number of measurement data, some statistic methods would be feasible to evaluate the

quality of those license bands. Furthermore, these measurement data could be used for cell planning in the initial stage of system development.

Cell planning is a complex and important issue in a wireless communication system. The planning quality directly effects on the system performance and economy efficiency. A properly designed system achieves the maximum coverage and capacity with minimum economy cost. For system optimization, there are much parameters need to be considered such as coverage, traffic density, rms(root mean square) delay spread, and interference scenario. Coverage and capacity are basic issues in cell planning for second-generation system, such as GSM system. However, in WCDMA system, rms delay is an important issue as well. Propagation with excessive rms delay spread results in serious inter-symbol interference (ISI) which degrades the communication performance or even makes a link failed. Besides, the coverage and capacity are sensitive to background noise power level in WCDMA system. Higher noise power will reduce the cell range and system throughputs.

Dealing with cell planning problem, an accurate propagation prediction model is required. Though there are a lot of prediction models for mobile propagation channel, the Walfisch-Ikegami propagation model [1, 2] has been verified for better prediction in an urban area with smaller cells. It is a hybrid model which is combined with diffraction down to street level and some empirical correction factors. It has been specifically adapted to short range cellular applications. With the help of this accurate prediction model, cell planner still need an optimization algorithm. Though there are several algorithms for optimization problems, genetic algorithm [3,4,5] is suitable for complex problem such as cell planning. GA is a nature-inspired algorithmic technique based on the principles of natural evolution. It is widely used to solve optimization problem. Thus, adopting an optimum algorithm and with the help of the accurate prediction model and digitized building information, cell planning would become easier.

In this paper, we will present the results of noise power level measurements conducted in urban Taipei city. The spectrum quality of each license band are

evaluated. Based on the measurement data, we use simple genetic algorithm with the help of Walfisch-Ikegami model and digitized building information to achieve the optimization of cell planning for WCDMA system. The proposed method can reduce the impact of background noise power and use minimum base station number to achieve the maximum coverage and data throughputs.

II. BACKGROUND NOISE FLOOR MEASUREMENTS IN URBAN TAIPEI CITY

For a system operator, it is necessary to evaluate the

spectrum quality before investing on 3G licenses. During the summer of 2001, we carried out lots of noise measurements on building top and at street level for FDD mode uplink bands and FDD mode downlink bands respectively in urban Taipei city. In the measurements on building top, we use spectrum analyzer, ADVENTEST U3641, to measure the noise power level at selected locations. The WCDMA uplink bands, including licenses A, B, C and D, are measured. At each location, the measurement is carried out at four directions, east, west, south and north. At street level, we use Agilent E7476A for noise measurements. It is a drive test solution for cdma2000 and WCDMA. The WCDMA downlink bands, including license A, B, C and D, are measured. And during the measurements, the frequency, power level and GPS coordinates are recorded in a notebook.

In order to analyzing large number of measurement data, several statistic parameters are calculated. The first one is the cumulative distribution function (CDF) of noise power level. We calculate the percentage of interference power level under a certain power threshold. For a cleaner spectrum, the CDF curve rises rapider. The second parameter is the statistics of power level crossing rate (LCR) [6]. We calculate the rate that the noise power envelope crosses a specified power level in a positive-going direction. However, the parameters of CDF and LCR are not enough for spectrum quality evaluation. In CDMA system, noise bandwidth is more important for communication quality. Therefore, the third parameter is the average of noise bandwidth. We calculate the average noise bandwidth in which the power levels are above specified power thresholds. Considering the LCR and average noise bandwidth at the same time, one can estimate the noise bandwidth and crossing times per unit bandwidth at different power thresholds, and then the spectrum quality can be evaluated.

Fig. Ⅰ is one of the interference power statistics, PDF, of license C. For uplink frequency band, the mean value is –109.4dBm as shown in TableⅠ. The maximum value, the minimum value, and the variance are –75.5dBm, –137.5 dBm and 18.7dB, respectively. For downlink frequency band, the mean value, maximum value, minimum value, and variance are –109.6dBm, –72.6dBm, –135.6dBm, and 15.7 dB.

We also analyze the impact of interference levels on system coverage and data throughputs for WCDMA system. For uplink coverage analysis, the signal to noise ratio(SNR) can be expressed as(1):

)1(⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅+⋅

⋅=CoChanneljj INoiseFloor

PathLossMSTxPwrRv

WSNR

where W is the chip rate of WCDMA system, vj is activity factor at physical layer, Rj is bit rate of usr j, MsTxPwr is mobile station transmitting power and ICoChannel is interference power coming from other users. The required path loss will be reduced when noise floor arise due to the fixed SNR requirement, mobile station transmitting power and system capacity. Fig. Ⅱ shows the relationship between noise floor and the required system path loss at different system loading. It is obvious that the system coverage range will be larger at lower noise floor.

For down link capacity analysis, we used (2):

)2(⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅+⋅

⋅=CoChanneljj INoiseFloor

PathLossBSTxPwrRv

WSNR

where BsTxPwr is base station transmitting power. The ICoChannel must be reduced when noise floor rise higher at fixed SNR requirement, mobile station transmitting power and system coverage range. Fig. Ⅲ shows the relationship between noise floor and system capacity at different coverage ranges. It is obvious that the system coverage and capacity will be higher at lower noise floor.

Table Ⅱ shows the system performance evaluation from measurement data for license A, B, C and D with uplink and down link bands. License D is the license with lowest mean value of noise power and results in maximum coverage for uplink band, and license A suffers from heavier impacts of background noise. For downlink, license D is the license with lower mean value of noise power, and license C suffers from heavier impacts of background noise. III. CELL PLANNING USING SIMPLE GENETIC

ALGORITHM

Genetic algorithm is a nature-inspired algorithmic techniques based on the principles of natural evolution. The individuals with better gene, which lead to fitter to the environment, will survive in evolution process. But individuals with worse gene will be eliminated in evolution process. After the elimination of natural environment, the survival with better gene will mate with each other and bear their children. The children will inherit parents’ gene. By iterating the previous operation, the best gene will be obtained in an ideal case.

The genetic algorithm used here is much likely as in [5]. Based on standard operations as selection, crossover and mutation, GA will solve optimization

problem easily. In the following, we briefly describe the main parameters used in the algorithm:

1) Gene Encoding: This operation will encode the

gene as string with N-ary alphabet according to the characteristics of an individual and then facilitates the operations after. For example, if some kind of insect with 8 types of wings then the gene would be encoded as binary string { 000 , 001 , 010, … , 110 , 111 }.

2) Fitness Function: It is a predefined linear or

nonlinear function which is used to evaluate the fitness quantity in the environment for every individual. For the example above, the fitness function evaluates the fitness quantity in the environment by considering about insects’ wing type and other characteristics such as head shape and feet length.

3) Crossover: This operation will recombine the

gene of parents for children. There are three basic crossover methods, One-point Crossover, Multi-point Crossover and Uniform Crossover. For the first method, it randomly chooses one crossover point but the second method chooses two. The third method uses a fixed mask for every crossover process and the process is shown as (4) for binary operation:

)4(212

211

⋅⋅⋅⋅⋅⋅⋅+⋅=

⋅+⋅=

MaskParentsMaskParentsOffspring

MaskParentsMaskParentsOffspring

4) Mutation: The mutation operation is used to

avoid the erroneous searching for global extreme value. For binary operation, the mutation process will invert some 1s to 0s or 0s to 1s in gene string.

The mentioned genetic algorithm above is used in our work to find out the optimized solution for cell planning. This proposed method is intended to find a solution that is impacted by less background noise and used minimum number of base stations to achieve specified coverage rate and maximum data throughputs. At the beginning, we select the possible locations for setting base station according to the digitized building information. In gene encoding process, base station location, height and transmitting power are encoded. There are 4 kinds of transmitting power available, 1W, 1.5W, 2W and 2.5W. The fitness function is designed as following:

( ) ( )

( )

−−−

−−=

−++

+=

≥

ref

jj

jj

jj

ref

jjj

j

ThroughputputAvgThrough

bleBSsTotalPossiBSsUnUsedofNumber

teCoverageRaresholdCoverageThss Fitne else

gOverlappinbleBSsTotalPossi

BSsUnUsedofNumber.

ThroughputputAvgThrough

.teCoverageRass Fitne

resholdCoverageThate CoverageRif

1

)(

101.0

30

300.3

) (

where CovergeRatej is the percentage of area covered

by the selected base stations, AvgThrouphputj is the average data throughputs of the base stations, Throuphputref is the throughputs with respect to the measured minimum noise power level, Overlapping is the percentage of area covered by 2 base stations or more, and CoverageThreshold is the desired coverage rate. In our method, the number of used base stations, coverage rate and data throughputs are concerned for optimum solution. If the selected individual achieves the specified coverage threshold, fitness function will optimized the fitness quantity by reducing base station number and increasing average data throughput. If the selected individual doesn’t achieve the specified coverage threshold, fitness function will evaluate the fitness quantity by increasing coverage rate. The fitness quantity of every individual could be evaluated and sorted in order, and then, the operation decides which individual to be survived or eliminated. The survival rate is set as 50%. For crossover, uniform crossover method is used. The mutation rate is 0.2. The algorithm iterates 2000 generations and then stops for outputting the solution.

IV. SIMULATION RESULTS

We use simple genetic algorithm with the help of Walfisch-Ikegami model and digitized building database to solve the optimization problem of cell planning. The selected area for simulation and validation is in the vicinity of NTUT campus. As is shown in Fig. Ⅳ, it is an area of 1.5kms by 1.6kms square digitized building map. Those blocks with different color represent different building height. The brighter ones represent higher buildings and darker ones represent lower buildings. The average building height is 17 meters and the standard deviation is 14 meters. In our simulation, we assume a compound traffic pattern with a mixed of 80% speech users (12.2kpbs user data rate), 15% of 144kpbs and 5% of 384kpbs data users. The different traffic pattern would result in different system performance. Then we evaluate each base station’s performance, such as coverage and throughputs [7], which are impacted by noise power. Finally, the characteristics of these possible base stations are encoded as gene strings and started evolution.

A selected result is shown in Fig. Ⅳ. There are 3 base stations needed for serving this area with mean transmitting power, 1W, and standard deviation, 0.5W. The dotted points are the area covered by the designed WCDMA system. The coverage rate is 92.4% and total data throughput is 7.9 Mbps.

V. CONCLUSIONS

For third generation mobile communication systems, the performance, such as coverage rate and data throughputs, are sensitive to noise power level. We have presented the results of noise power measurements in urban Taipei city for WCDMA

systems. The measurements include FDD mode uplink and downlink frequency bands of 3G licenses issued in Taiwan. We compile the statistics of the measurement data and analyze the impact of background noise power levels on coverage and data throughputs for WCDMA system. The spectrum quality evaluation using measurement data for uplink and downlink bands of license A, B, C and D are presented. Also, a cell planning method based on simple genetic algorithm with the help of Walfisch-Ikegami model and digitized building information is developed. A selected example is examined to show that our proposed method can reduce the impact of background noise power and use minimum base station number to achieve the maximum coverage and data throughputs. Thus, an easy and efficient cell planning and system enhancement method for WCDMA systems can be delivered. Furthermore, the deployed system would suffer from less impact of background noise power and achieve maximum performance with minimum cost.

ACKNOWLEDGMENTS This research is sponsored by Taiwan Cellular Co., Taiwan, under Contract R90004.

REFERENCES [1] S.R. Saunders, “Antennas and propagation for wireless

communication systems,” John Wiley & Sons, pp.170-171, 1999.

[2] D.Har, A.M. Watson, and A.G. Chadeny, “Comment on diffraction loss of rooftop-to-street in cost 231 Walfisch-Ikegami model,” IEEE Trans. Veh. Tech., vol. 48, pp. 1451-1452, 1999.

[3] K. F. Man, K. S. Tang and S. Kwong, “Genetic Algorithms,” Springer, 1999.

[4] R. L. Haupt, and S. E. Haupt, “Practical Genetic Algorigth,” John Wiley & Sons, 1998.

[5] Calegari P., Guidec F., Kuonen P., and Wagner D., “Genetic Approach to Radio Network Optimization for Mobile Systems,” IEEE Veh. Tech. Conf.,vol. 2, pp. 755 –759, 1997.

[6] T. S. Rappaport, “Wireless communication principles and practice,” Prentice Hall PRT, 1999.

[7] Harry Holma and Antti Toskala, “WCDMA for UMTS—Radio Access for Third Generation Mobile Communications,” John Wiley & Sons.

Fig. Ⅰ. PDF of noise power level of 3G spectrum for license C.

TABLE Ⅰ

BACKGROUND NOISE POWER STATISTICS OF 3G LICENCES

Mean Value (dBm)

Max. Value (dBm)

Min. Value (dBm)

Variance(dB)

FDD Uplink -108.1 -66.7 -141.8 24.4 License

A FDD Downlink -110.5 -76.7 -138.6 14.4

FDD Uplink -108.8 -77.8 -134.5 19.9 License

B FDD Downlink -110.2 -82.5 -135.8 14.2

FDD Uplink -109.4 -75.5 -137.5 18.7 License

C FDD Downlink -109.6 -72.6 -135.6 15.7

FDD Uplink -109.6 -75.5 -135.8 17.1 License

D FDD Downlink -111.2 -85.2 -141.5 14.2

Fig. Ⅱ. Maximum path loss vs. noise floor for uplink

Fig. Ⅲ. Throughputs vs. noise floor for downlink

TABLE Ⅱ PERFORMANCE OF 3G LICENCES

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. Path-Loss

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

Thro-ughput(Mbps)

Lmax=145 2.48 2.59 2.52 2.58 2.55 2.55 2.55 2.61

UL:Uplink DL:Downlink

Fig. Ⅳ. A cell planning example