ACTIVIDADES

In this investigation of CDMA system capacity, the number of users the system can support is evaluated using a computer simulation. The cell radii in all scenarios are normalized to unity. The conventional hexagonal cell pattern is assumed. No mobility model is considered for this capacity simulation. A uniformly distributed mobile population is generated with random locations within home and the 18 neighbour sectors. This is done by generating random numbers that assign an angular position and a radial distance to each MS with respect to the home BS (see Fig. 4.4). The individual path losses (coupled with the shadowing effect) are calculated for each MS in order to evaluate the SIR at sector BSs. The simulation software was written in MATLAB employing random number generators to represent call arrivals. Simulations for three adaptive sector configurations were performed to estimate the uplink capacity taking both intra-sector and inter-sector interference into account. The sector coverage is obtained by using adaptive antennas in practical settings with associated antenna patterns including sidelobes. For each loading (in terms of users), we ran simulation more than 20,000 times and obtained an average value for the blocking probability.

32 34 36 38 40 42 44 10−3

10−2 10−1

Blocking probability

Average active number of users per sector

Figure 5.16: Blocking probability of the system with voice users only.

of voice users with constant bit rate (9.6 kbps). The voice activity factor is taken to be 37.5%. It can be seen from the plot that the capacity of system is 35 voice users per sector (or 105 user per cell) at 1% blocking and 37 users (or 111 users per cell) at 5% blocking. In Fig. 5.17, we plot the blocking experienced when only one data user per sector (operating in circuit switched mode) at 38.4 kbps and 76.8 kbps respectively (8₋codes

and 2 _×8₋codes aggregated, each at 9.6 kbps). Since the M ₋codes corresponding to the data user are active all the time, the activity factor of the circuit switched mode data user is 1. We observe that when the data user is operating in circuit switched mode, the system can support 23 and 12 users withM = 8 andM = 16, respectively, at 1% blocking. In order to integrate voice and data service, we will have only one data user, and use all the remaining capacity for voice users. Fig. 5.18 shows the number of voice users

10 15 20 25 30 35 40 45 10−3

10−2 10−1

Blocking probability

Average active number of users per sector

M = 16 M = 8 _{M = 0}

Figure 5.17: Blocking probability with one high speed data channel at 4 times and 8 times normal bit rate, and data activity factor = 1.

against the number of parallel codes in a multi cells system. We see that there is a linear relationship between the number of voice users and the number of multi codes used in parallel transmission. Our simulation agrees with the results shown in [37] although the results of [37] is focused on single cell systems only.

We study the system behaviour as the high data rate user is in a Hot Spot Sector (HSS) and uniform traffic persists with normal bit rate,Rb, in every neighbour cell. The results

of Fig. 5.19 indicate how the possible number of users in HSS decreases with increasing bit rate of the high data rate user in the HS.

The simulation also shows that in this situation (at 1% blocking), there is a capacity improvement of about (37 - 23)/23 = 60% and (18 - 11)/11 = 63% atM = 8 andM = 26, respectively, with adaptive sectorisation. At data user data rate of about 24 times the

0 5 10 15 20 0 5 10 15 20 25 30 35 40

Number of users per sector

Number of parallel codes

Figure 5.18: Number of users per sector Vs number of parallel codes at 1% blocking probability.

0 10 20 30 40 50 60 10−4 10−3 10−2 10−1 100 Blocking probability

Active number of users per sector

HSS = 600 HSS = 1200 M = 24

M = 16 M = 8

Figure 5.19: Adaptive sector with HSS = 600

Vs fixed sector (with HSS = 1200

). Blocking probability with only one high speed data channel atM = 8, 16, and 24 times the normal bit rate. (data activity factor = 1.)

to be expected because the interference caused by the data user is excessively large.

5.5

Conclusions

Adaptive sectorisation can be used to improve the capacity of a CDMA cellular system when a cell or a sector contains an area of congested traffic (i.e., a hot spot). A simple and robust technique to achieve adaptive sectorisation is to employ finite beam switching with a suitable array antenna at the sector BS. The available capacity improvement is a function of the ratio of the user densities in the congested and non-congested areas of the sector and it appears that the improvement is particularly significant when the user density in the congested area is an order of magnitude higher than that of the rest. A simple beam forming dipole array antenna structure such as the one considered here, can be used for the implementation of adaptive sectorisation. Adaptive sectorisation can also be used for capacity improvement in multi-rate CDMA systems, that use multi-codes.

Capacity of WCDMA Cellular

Systems

To investigate the capacity of WCDMA cellular systems it is necessary to examine the air interface of third generation (3G) cellular systems. This chapter gives an overview of the emerging 3G cellular systems and draws attention to IMT-2000 standard which employs WCDMA as its air interface [39]. WCDMA is also known as UMTS Terrestrial Radio Access (UTRA) scheme.

6.1

WCDMA Air Interface

WCDMA has been widely accepted as the air interface for the third generation cellular systems. Its specifications have been drawn by the joint standardisation body called 3GPP (3rd _{Generation Partnership Project).}