CELLULAR COMMUNICATION NETWORKS DESIGN in .NET

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CELLULAR COMMUNICATION NETWORKS DESIGN
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Frequency assigment span (Radius 100 m) adjacent cells 500 450 Number of channels 400 350 300
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L~d-3 L~d-4 WIM
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FIGURE 12.14. Frequency assignment for adjacent cells.
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assignments for the worst situations with a con guration of cellular pattern planning, such as overlapped and adjacent, where the two-ray model is a weaker predictor. Consequently, for nonuniform and nonregular radio cellular networks it is more realistic to use the stochastic model (which also predicts a distance dependence of d 2:5 in the presence of the diffraction phenomena, that is close to that for
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L~d-3 L~d-4 WIM
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Frequency assigment order (Radius 400 m) overlapping cells 500
Number of channels
440 432
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FIGURE 12.15. Frequency assignment for overlapping cells.
WIM $ d 2:6 ) compared with that predicted by the simple two-ray model usually used by other authors [34,38]. So, we show again, as was done in above discussions of how to predict the ef ciency and increase performance of cellular networks, that the strict description of propagation phenomena occurred in speci c urban radio communication channels allow designers to better and more precisely resolve both
CELLULAR COMMUNICATION NETWORKS DESIGN
the base station location problem and the frequency assignment problem, which must be considered simultaneously. We may conclude that the receipt based on the heuristic model, which was discussed in References [31 33] is fully veri ed by existing experimental data described in Reference [34], as well as by other theoretical models [35 38]. It can be used for the purposes of nonuniform cellular maps design and channel (frequency) assignment within different land radio links by the use of the more realistic propagation models, the WIM (as the COST-231 standard) and the stochastic multiparametric model. The results shown in Figures 12.13 12.15 fully illustrate the actuality of these conclusions.
12.3. PREDICTION OF PARAMETERS OF INFORMATION DATA STREAM In the literature for wireless communications, the term capacity has different meanings: determining the user capacity in cellular systems in users per channel, information stream capacity inside the communication channel in bits per second, or considering data in bits per second per hertz per base station dealing with the spectral ef ciency of the communication channel. Let us determine the main parameters of the information data stream sent through any wireless communication link. Channel Capacity and Spectral Ef ciency According to standards utilized in information science, a channel capacity, denoted by C, is referred to the maximum data rate of information in a channel of a given bandwidth, which is measured in bits per second (bps). Whereas the spectral e ef ciency, denoted by C C=Bw, is considered as a measure in bits per second per hertz (bps/Hz). Both these terms are used in the well-known Shannon Hartley equation, which for one channel with the given signal-to-noise ratio (SNR) S=N0 Bw , where S is the signal power in W J=sec, Bw is the channel bandwidth (in Hertz) and N0 is the noise power spectral density in W=Hz, can be written as [39]   S C Bw log2 1 12:20 N0 Bw e If we denote C as the spectral ef ciency, as the ratio   e log2 1 S C N 0 Bw
C Bw ,
we get instead of (12.20) 12:21
These two formulas for the capacity and spectral ef ciency estimation are valid only for the channels with additive white Gaussian noise (AWGN-channels), which is also called additive noise (see de nitions in 1). In this case the power of the additive noise equals Nadd N0 Bw , which is simply de ned in the literature as the signal-to-noise ratio (SNR). Usually, AWGN channels are called the ideal
PREDICTION OF PARAMETERS OF INFORMATION DATA STREAM
channels and all practical radio channels are compared to the ideal channel by selecting detection error probability of 10 6 and nding SNR necessary to achieve it. Effects of interference can be regarded as another source of effective noise, which raises the noise level for calculating the error rate. In this case, we must also introduce in (12.20) and (12.21) together with Nadd the noise caused by interference Nint   S e 12:22 C log2 1 N0 Bw Nint
Above, we discussed the channels in which only white or Gaussian noise was taken into account. What will happen if there is additional noise called multiplicative (see de nitions in 1), which usually occurs in the wireless communication channel, land, atmospheric, and ionospheric due to multipath fading phenomena In this case, on the basis of a uni ed algorithm of how to estimate fading effects, described in 11, we can account for all kinds of noise in the Shannon Hartley formula (12.20). To do that, we propose now a simple approach, which can be used only if LOS component is predominant with respect to the NLOS component, that is, when the Ricean parameter K > 1 [40]. Taking into account the fading phenomena, described by the Ricean distribution with parameter K > 1 (see s 1, 5, and 11), we can estimate the multiplicative noise by introducing its spectral density, Nmult , with its own frequency band, BO , into (12.20) or (12.21) ! S e log2 1 12:23 C N0 Bw Nmult BO This formula can be rewritten as   !   S Nadd Nmul 1 e C log2 1 log2 1 S S Nadd Nmul 12:24
where, according to our de nitions introduced in s 5 and 11 following hIco S the proposed stochastic approach, Nmult hIinci . Using now the de nition of K, i introduced in these chapters, we get S hIco i K Nmult hIinc i 12:25
Combining together all these notations, we nally get the capacity as a function of the Recean K-factor   1 !   Nadd K SNRadd 1 12:26 K Bw log2 1 C Bw log2 1 S K SNRadd where we denoted signal-to-additive-noise ratio as SNRadd NS . add