No. 1, Sec. 3, Chung-Hsiao E. Road, Taipei, Taiwan. Taiwan. Abstract WCDMA is an interference-limited system with coverage and data

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1 A Cell Planning Scheme for WCDMA Systems Using Genetic Algorithm and Measured Background Noise Floor Hsin-Piao Lin 1, Rong-Terng Juang 1, Ding-Bing Lin 1, Cheng-Yi Ke 2 and Yi Wang 2 1 Institute of Computer, Communication and Control, National Taipei University of Technology. No. 1, Sec. 3, Chung-Hsiao E. Road, Taipei, Taiwan. hplin@en.ntut.edu.tw, s @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. 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.

2 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 adacent 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.

3 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 adusts 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

4 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,

5 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 is given by [12] SNR ( Rx) W P = (1) v R P + I NF ( c)

6 where W is the chip rate, 3.84Mcps, v is the activity factor at physical layer, 0.67 for speech and 1.0 for data, (Rx) R is the bit rate, P is the received power from user, P NF is the background noise floor including thermal noise and noise from any (c) man-made transmitters within other communication systems, and I, the co-channel interference associated with user, is defined as the interference power coming from other links operating at the same frequency band within the same system. The total received power I total can be expressed as the summation of the background noise floor, co-channel interference, and the received power from user, i.e. I + ( c) ( Rx) ( Rx ) total = PNF + I P. Defining P = L Itotal, the load factor L of one link has the form L = 1+ ( E b 1 W / N ) 0 R v (2) where ( E / N 0 ) is the SNR of link. For N users with the same traffic type in the cell, b the system loading η is N L = 1 η = (3) The received power excluding the background noise floor can be given by I total P = η I (4) NF total 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.

7 According to (2), the averaged load factor, L, of one link with respect to the specified traffic pattern is 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, I total, can be obtained using (4) if the system loading and the background noise floor are given. Afterward the minimum received power from user is determined by P ( = L I. Given the mobile transmitting Rx) total (Rx) power as 0.5W, the maximum allowed path loss can be evaluated because P 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 ( Rx) I is solved by P = L Itotal. Once the background noise total 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

8 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

9 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

10 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 F i 2 ( avg) Ti max( Ci CT,0) 1 = C i + Tref Ci C (5) T Bi where C i is the percentage of the covered area, (avg) T is the average data throughput, P avg is the average transmitting power of used BSs, T ref is the throughputs with respect to the measured minimum noise power level, C T 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

11 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

12 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 REFERENCES [1] B. Smida, V. Sampath and P. Marinier, Capacity degradation due to coexistence between second generation and 3G/WCDMA systems, in Proc. IEEE Veh. Technol. Conf., vol. 1, May 2002, pp [2] K. Heiska, H. Posti, P. Muszynski, P. Aikio, J. Numminen and M. Hamalainen, Capacity Reduction of WCDMA Downlink in the Presence of Interference From Adacent Narrow-Band System, IEEE Trans. on Veh. Technol., vol. 51, Issue 1, pp , Jan

13 [3] E. Amaldi, A. Capone and F. Malucelli, Optimizing Base Station Siting in UMTS Networks, in Proc. IEEE Veh. Technol. Conf., vol. 4, May 2001, pp [4] C. Y. Lee and H. G. Kang, Cell Planning with Capacity Expansion in Mobile Communications: A Tabu Search Approach, IEEE Trans. Veh. Technol., vol. 49, no. 5, pp , Sep [5] M. J. Nawrocki, and T. W. Wieckowski, Optimal site and antenna location for UMTS output results of 3G network simulation software, in Proc. Int l Conf. Microwaves, Radar and Wireless Commun., vol. 3, May 2002, pp [6] Z. Altman, J. M. Picard, S. Ben Jamaa, B. Fourestie, A. Caminada, T. Dony, T, J.F. Morlier and S. Mourniac, New challenges in automatic cell planning of UMTS networks, in Proc. IEEE Veh. Technol. Conf., vol. 2, Sept. 2002, pp [7] E. Amaldi, A. Capone, E. Malucelli, and F. Signori, UMTS radio planning: optimizing base station configuration, in Proc. IEEE Veh. Technol. Conf., vol. 2, Sept. 2002, pp [8] Hsin-Piao Lin, Ding-Bing Lin and Rong-Terng Juang, Performance Enhancement for Microcell Planning Using Simple Genetic Algorithm, in Proc. IEEE Antennas and Propagat. Society Int l Symp., Vol. 4, Jun. 2002, pp [9] J. H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor: Univ. of Michigan Press, [10] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Reading, Mass.: Addison-Wesley, [11] R. L. Haupt, and S. E. Haupt, Practical Genetic Algorigth, John Wiley & Sons, [12] Harri Holma and Antti Toskala, WCDMA for UMTS Radio Access for Third Generation Mobile Communications, John Wiley & Sons

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15 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

16 Table 2 An Example of Performance Evaluation of the 3G License Bands Issued in Taiwan Max. Allowed Path Loss (db) Max. Throughputs (Mbps) 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 60% system loading 70% system loading L max = L max = L max = L max = UL:Uplink DL:Downlink

17 Fig. 1. CDFs of the background noise power levels. a) Uplink frequency bands and b) downlink frequency bands.

18 Fig. 2. FD-LCRs of the background noise envelopes at different power thresholds. a) Uplink frequency bands and b) downlink frequency bands.

19 Fig. 3. Average bandwidth of the background noise at different power thresholds. a) Uplink frequency bands and b) downlink frequency bands.

20 Fig. 4. Maximum allowed path loss at different background noise power floors for different system loadings in the uplink WCDMA system.

21 Fig. 5. Maximum data throughput at different background noise power levels for different path loss (L max ) in the downlink WCDMA system.

22 Fig. 6. Flowchart of the proposed cell planning algorithm.

23 Fig. 7. A cell planning example. Three BSs cover the dotted points.