 although with more sophistication, we also need to estimate the number of concurrent channels required for PS traffic. This number of channels will correspond to Deploying 3G Networks the peak traffic during a Busy Hour (BH), which as in the CS, we determine also from the traffic assumptions of the offered load during the busy hour per cell expressed in kbits. In general, we treat each service independently to meet the different grade of ser- vice or asymmetry required. We calculate the number of PS service channels by accounting a duration window cor- responding to an acceptable delay (e.g. d 12 0.5-07 s) for a given service. From the prin- ciples outlined in Chapter 2, we can illustrate the calculation for WWW application6 as follows. We take 384 kps service with packet length 5= 480 bytes. From the total BH traffic for a given reference area we calculate the mean offered data rate m in kbps. Translating this into a mean packet arrival p rate, i.e. p = (m X d)IS.. Then assuming a Poisson packet arrival distribution for all users, with a mean p, we obtain the probability density function (PDF), as well as the cumulative density function (CDF). Figure 7.3 illustrates the peak packet arrival rate h at 95% time probability [7]. P(k Arrivals in sec) [PDF] Downlink / Poisson Distribution, t = 0.5, A=17 0.104 0.096 0.088 P(k) (PDF) 0.072 0.064 0.056 0.048 0.032 0.024 0.016 0.008 k Arrivals in sec Figure 7.3 Peak arrival rate. Utilizing the upper 95% time probability of the packet arrival rate (Figure 7.3) and ap- plying the typical packet length we translated back into kbps. We then calculate the number of channels (ch) dividing by the service bearer rate r, i.e. ch = h (kbps)/r. We can summarize the process as: Chs = (1/Serv Rate) X (1/Serv Delay) X CDFp (m/Serv delay X 5),95%), where CDFp(x,y) corresponds to the point of probability on the CDF associated with Poisson's law of mean X, and where m represents the mean offered data rate in kbps. We should note here that this process can be inefficient with low traffic 