Performance Analysis of UMTS Cellular Network using Sectorization Based on Capacity and Coverage in Different Propagation Environment

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1 Performance Analysis of UMTS Cellular Network using Sectorization Based on Capacity and Coverage in Different Propagation Environment M. S. Islam 1, Jannat-E-Noor 2, Soyoda Marufa Farhana 3 1 Assistant Professor, Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh 2&3 Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh ABSTRACT Performance analysis of umts network is of major interest, because of the WCDMA technique used in umts, which leads to an interference limited system with a dynamic cell capacity and load dependent cell coverage. The performance of umts network depends on sectorization; also, the coverage area depends significantly on the geografical nature and the propagatoin environment of the covered area. In this paper, the capacity and coverage of umts cellular network covering a densed urban area and suburban area are simulated for incrising amount of sectorization showing the number of users and coverage area gradually increased. For modelling the propagatoin, the cost-231 hata model has been used. Key Words: UMTS, Coverage, Propagation Model, WCDMA, Sectorization. 1. INTRODUCTION Universal Mobile Telecommunications System (UMTS) is one of the standards in 3rd generation partnership project (3GPP). This thesis presents the performance of UMTS cellular network using sectorization for capacity and coverage. The major contribution is to see the impact of sectorization on capacity and cell coverage with independent dynamic parameters as energy per bit to noise spectral density ratio, soft handover factor, voice activity factor, intercell or outercell interference factor, data rates. Bo Hagerman, Davide Imbeni and Jozsef Barta considered WCDMA 6-sector deployment case study of a real installed UMTS-FDD network [1]. Romeo Giuliano, Franco Mazzenga, Francesco Vatalaro described Adaptive Cell Sectorization for UMTS Third Generation CDMA Systems [2]. Achim Wacker, Jaana Laiho-Steffens, Kari Sipila, and Kari Heiska considered the impact of the base station sectorization on WCDMA radio network performance [3]. S. Sharma, A.G. Spilling and A.R. Nix considered Adaptive Coverage for UMTS Macro cells based on Situation Awareness [4]. A.K.M Fazlul Haque, Mir Mohammad Abu Kyum, Md. Baitul Al Sadi, Mrinal Kar and Md. Fokhray Hossain considered UMTS coverage and capacity based on sectorization[5]. Most of the works analyzed the Copyright 212, Asian Business Consortium AJASE Page 48

2 performance considering sectors with static parameters but it is needed to analyze the performance along with all dynamic parameters and propagation environment. This paper focuses on main factors that affect the coverage and capacity in a CDMA cell based on sectorization and on propagation prediction models COST-231 Hata model. 2. CAPACITY AND COVERAGE CALCULATIONS The capacity of a CDMA cell depends on many different factors, such as power control accuracy, interference power. In this present study we are considering perfect power control. We begin by calculating the signal-to-noise (interference) power [7, 8]. 2.1 Initial Model for Capacity Calculation: In order to calculate the maximum number of users in a particular cell the following assumptions are made: No inter-cell and intra-cell interference is present within the cell. All signals arrive at the base station with equal power Un-limited number of spreading codes are available. If there are Ns users in a cell and the signal is denoted by S then the interference can be calculated as I = (Ns 1)S +η, where η is the thermal noise. Hence the SIR is given by s 1 SNR = (1) ( N 1 s ( N s 1) / S s ) Suppose the digital demodulator for each user can operate against the noise at energy per bit-to noise power density level is given by Eb/No, where Here Eb = S / R and No = I / W Eb/No= S / R I / W... (2) Where, W is the chip rate, R is rate of data communication and I is interference power of the cell. Hence using equations (1) and (2) gives W / R Ns - 1= E -.. (3) b /N o S For a uniform population, this reduces the average signal power of all users and consequently the interference received by each user. This results in an increase in the Eb/No by a voice activity gain factor, α. Similarly, the cell sectoring factor D also increases the Eb/No. Finally, we must evaluate the interference mathematically, interference from other cell β= interference from given cell Due to the interference, the actual numbers of user will decreases. It is also necessary to consider the affects of soft handover factor (H), Array antenna gain (Ag).Thus the capacity for WCDMA in UMTS yields: W / R D H A g NS= 1+ ( E... (4) 1 b /N o - S ) 2.2 Coverage versus Capacity: The analysis in the above the capacity calculation can be isolated from coverage. We can understand the performance of a WCDMA network by developing a simple expression for the ratio S as follows. Copyright 212, Asian Business Consortium AJASE Page 49

3 S= ( Eb No ) R ( Eb No) R S W D H A N 1 1 g... (5) We focus on the coverage by user 1 when the number of users in the cell is Ns. Let r be the distance of user 1 from the base station. The received power at the base station from mobile user l, S, is given by S = S1 P (d) Z... (6) Where, S1 the transmission power of the user, P (d) is the propagation loss at distance d from the MS to BS, Z the shadow fading. 2.3 Coverage Area in different Propagation Environment: The propagation losses in densed urban and suburban areas are usually calculated by using propagation models. In the present study we utilized COST 231-Hata model for urban and dense urban environment. Where higher data rates need higher processing gain resulting in smaller coverage area. But increasing sectors with same parameters makes extensive coverage for higher data rates. The COST-Hata-Model is formulated as, For densed urban environments the path loss: L = log (f) log hb [log (11.75h UE )] ( log hb) log d (7) For suburban or rural environments the path loss: L = log (f) log hb {(1.1 log f.7) hue - (1.6 log f -.8)} + ( log hb) log d... (8) Where d is the coverage radius and R is the data rates. After calculating the cell range d, the coverage area can be calculated. The coverage area for one cell in hexagonal configuration can be estimated with [6] Coverage area, A =K.d 2... (9) Where A is the coverage area, d is the maximum cell range, and K is a constant. K values for the site area calculation [6]: K=2.6, 1.3, 1.95, 2.6 for sector one, two, three, four respectively. 3. SIMULATION & RESULT The analysis has been done for capacity and coverage with sectoring cell for dense urban and suburban area using MATLAB R28a. From this figure 1 it is observed the Number of simultaneous 384 Kbps users vs. Eb/No in sectors cell. Copyright 212, Asian Business Consortium AJASE Page 5

4 Number of simultaneous 384 kbps users Number of simultaneous 384 kbps users Number of simultaneous 384 Kbps users vs. Eb/No in UMTS cell without sector 3 sectors 5 sectors 7 sectors Eb/No Figure 1: Number of simultaneous 384 Kbps users Vs. Eb/No in sectors cell Table 1: simulated values for Figure 1 Eb/No User With Out sector 4 Sectors 6 Sectors 8 Sec-tors The interference from other cell is known as inter-cell interference (β). Figure 2 represents, Number of simultaneous 384 Kbps users vs.inter-cell interference in sectors cell 1 4 Figure 2: Number of simultaneous 384 Kbps users vs. inter-cell interference in sectors cell Table 2: simulated values for Figure 2 β without sector 3 sectors 5 sectors 7 sectors Intercell interference factor User With User With Out sector 2 sector 4 sector 6 sector 8 Sector Copyright 212, Asian Business Consortium AJASE Page 51

5 Number of simultaneous 384 kbps users Number of simultaneous 384 kbps users Figure 3 shows that for increasing H and changing value of sectorization the number of simultaneous 384 Kbps data users increases. 1 3 Number of simultaneous 384 Kbps users vs. Handover factor in UMTS cell Figure 3: Number of simultaneous 384 Kbps users vs. soft handover factor in sectors cell Table 3: simulated values Figure 3 H Handover factor User With Out Sector 4 Sectors without sector 3 sectors 5 sectors 7 sectors 6 Sectors 8 Sectors Number of simultaneous voice users vs. voice activity factor in sectors cell. without sector 3 sectors 5 sectors 7 sectors Voice activity factor Figure 4: Number of simultaneous voice users vs. voice activity factor in sectors cell. Table 4: simulated values for Figure 4 α User With Out Sector Copyright 212, Asian Business Consortium AJASE Page 52

6 Coverage area in square km & cell range in km Coverage area in square km & cell range in km Finally, consider for coverage vs. data rates in dense urban area and suburban area, where operating frequency is considered 2 MHz with COST 231 Model as a radio propagation model. Coverage vs. bit rates for dense urban using COST 231 model in sectors cell Cell area without sector Cell area using 2 sector Cell area using 3 sector Cell area using 4 sector Cell range Data rate in kbps Figure 5: Coverage vs. bit rates for dense urban using COST 231 model in sectors cell Table 5: simulated values for Figure 5 Data rate Kbps Cell range In (km) Area with-out sector (km 2 ) Area with 2 Sec-tors (km 2 ) Area with 3 Sec-tors (km 2 ) Area with 4 Sec-tors (km 2 ) Coverage vs. bit rates for suburban using COST 231 model in sectors cell 1.8 Cell area without sector 1.6 Cell area using 2 sector Cell area using 3 sector 1.4 Cell area using 4 sector Cell range Data rate in kbps Figure 6: Coverage vs. bit rates for sub urban using COST 231 model in sectors cell Table 6: simulated values for Figure 6 Data rate (Kbps) Cell range in (km) Area without sector (km 2 ) Area with 2 sectors (km 2 ) Area with 3 sectors (km 2 ) Area with 4 sectors (km 2 ) Copyright 212, Asian Business Consortium AJASE Page 53

7 Cell coverage range in km Coverage area in square km Coverage vs. bit rates for dense urban & suburban environment for a cell using four sectors 1.8 Densed urban area 1.6 Suburban area Data rate in kbps Figure7: Coverage vs. bit rates for dense urban & suburban environment for a cell using Table 7: simulated values for Figure 7 Data rate (Kbps) Coverage in densed Urban areas in km 2 Coverage in sub Urban areas in km Coverage range vs. bit rates for dense urban & suburban environment for UMTS a cell.9 Densed urban area.8 Suburban area Data rate in kbps Figure8: Coverage range vs. bit rates for dense urban & suburban environment. Table 8: simulated values for Figure 8 Data rate (Kbps) Coverage in densed Urban areas in km Coverage in sub Urban areas in km 4. CONCLUSION In this paper the coverage and capacity in a CDMA cell based on sectorization and propagation prediction models COST-231 Hata model is analyzed. It has been seen that, the performance of an UMTS network can be improved using sectorization. It is also observed that, both the coverage area and coverage range is more in a suburban area than a densed urban area. Copyright 212, Asian Business Consortium AJASE Page 54

8 5. FUTURE WORK Although this research tried to give an impression of the main factors affecting the capacity and coverage. For future research more attention has to be drawn to quality of service requirements in the system and more accurate model can be used for evaluating path loss. Table 9: Parameters used in our simulations Parameter Value Eb/No 3 db Frequency 2 GHz Chip rate 3.84 Mcps voice activity (α) 1 thermal noise (η) signal power (S1) 21dbm shadow fading 8db inter-cell interference (β).1 cell range(d) 2km base antenna height (hb) 2m user antenna height (hue) 2m antenna gain (Ag) 2db data rate (R) 12.2,64,144,384,2kbps sector (D) [ ] REFERENCES [1] Bo Hagerman, Davide Imbeni and Jozsef Barta WCDMA 6 - sector Deployment- Case Study of a Real Installed UMTS-FDD Network IEEE Vehicular Technology Conference, spring 26. [2] S. Sharma, A.G. Spilling and A.R. Nix Adaptive Coverage for UMTS Macro cells based on Situation Awareness, IEEE Vehicular Technology Conference, spring 21, page(s): [3] A. Wacker, J. Laiho-Steffens, K. Sipila, K. Heiska, "The impact of the base station sectorisation on WCDMA radio network performance", IEEE Vehicular Technology Conference,September [4] Romeo Giuliano, Franco Mazzenga, Francesco Vatalaro, Adaptive cell sectorization for UMTS Third generation CDMA systems IEEE Vehicular Technology Conference, May 21. [6] Jaana Laiho, Achim Wacker, Tomas Novosad, Radio Network Planning and Optimisation for UMTS -Second Edition John Wiley & Sons. [7] Rappaport T.S; Wireless Communications: Principles and Practice, Prentice Hall, 22. [8] Gilhousen K.S,et al; On the Capacity of a Cellular CDMA System, IEEE Trans. on VT, Vol. 4, No. 2, pp , May Asian Business Consortium is an independent research house committed to publishing and delivering superior, Peer-reviewed standard research Copyright 212, Asian Business Consortium AJASE Page 55