CELLULAR COMMUNICATION NETWORKS DESIGN

Qr-codes barcode library for .netUsing Barcode Control SDK for .net vs 2010 Control to generate, create, read, scan barcode image in .net vs 2010 applications.

Frequency assigment span (Radius 100 m) adjacent cells 500 450 Number of channels 400 350 300

Qr Codes generating on .netgenerate, create qrcode none for .net projects

282 354 341 328 467 459 455

QR Code ISO/IEC18004 recognizer in .netUsing Barcode scanner for .net vs 2010 Control to read, scan read, scan image in .net vs 2010 applications.

L~d-3 L~d-4 WIM

Generate bar code for .netgenerate, create bar code none on .net projects

250 200 0

.net Vs 2010 bar code readerwith .netUsing Barcode scanner for VS .NET Control to read, scan read, scan image in VS .NET applications.

242 238

Control qrcode data with visual c#.net denso qr bar code data on visual c#.net

1 2 Interference considered

Aspx.net denso qr bar code implementwith .netgenerate, create qr code none in .net projects

Frequency assignment order (Radius 250 m) adjacent cells 400 380 Number of channels 360

Control qr code data with vb.netto build quick response code and denso qr bar code data, size, image with vb barcode sdk

L~d-3 L~d-4 WIM

Qrcode barcode library in .netusing .net vs 2010 crystal todeploy qr codes with asp.net web,windows application

331 327

Develop barcode 39 with .netusing .net framework crystal tocreate bar code 39 in asp.net web,windows application

340 320 300 280 260 240 220 200 0

Barcode barcode library with .netgenerate, create bar code none on .net projects

242 238 282

341 328

Visual Studio .NET (WinForms) Crystal pdf 417 implementationwith visual c#.netusing .net for windows forms crystal toattach barcode pdf417 for asp.net web,windows application

1 2 Interference considered

.NET pdf417 scannerfor .netUsing Barcode reader for .net framework Control to read, scan read, scan image in .net framework applications.

FIGURE 12.14. Frequency assignment for adjacent cells.

decoding datamatrix 2d barcode on .netUsing Barcode reader for Visual Studio .NET Control to read, scan read, scan image in Visual Studio .NET applications.

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

Control data matrix barcodes size in microsoft wordto include datamatrix 2d barcode and ecc200 data, size, image with office word barcode sdk

PROPAGATION ASPECTS OF CELL PLANNING

Frequency assignment span (Radius 100 m) overlapping cells

650 600 Number of channels 550 500 450 400 350 300

Barcode reader on javaUsing Barcode reader for Java Control to read, scan read, scan image in Java applications.

399 360 439 437 601 599

Microsoft Excel ean 128 integratingon microsoft excelgenerate, create ean/ucc 128 none with microsoft excel projects

L~d-3 L~d-4 WIM

1 1.5 Interference considered

Frequency assigment order (Radius 400 m) overlapping cells 500

Number of channels

440 432

L~d-3 L~d-4 WIM

397 388

394 375

1 2 Interference considered

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