Mobile Wireless Communications in .NET Embed Code-128 in .NET Mobile Wireless Communications

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Mobile Wireless Communications using barcode creator for visual studio .net control to generate, create code-128c image in visual studio .net applications. barcode pdf417 We indicated that each BS Code-128 for .NET requires a received power PR , assumed to be the same throughout the system. This then determines the power required to be transmitted by a mobile a distance r m from its base station.

For recall, from (2.4) in 2, that the received power may be written, ignoring the rapid fading term PR = PT r n 10z/10 (6.10).

PT is the mobile s transmi tted power, n is the propagation exponent, usually taken as 4, and z is the gaussian-distributed shadow-fading random variable, centered on the average power, with standard deviation , in dB. Its probability density function is thus 2 2 f (z) = e z /2 / 2 2 (6.11) Now focus on the interfering mobile located at point (x, y), at a distance r1 from its BS in cell 1.

Let the shadow-fading random variable in this case be written as z1 . The mobile s transmitted power must be given by. n PT1 = PR r1 10 z1 /10. (6.12). (Note, by the way, that, s .net framework Code 128 Code Set B ince the shadow-fading random variable z is symmetrically distributed about 0, one could equally well write 10 z/10 in (6.10) and 10z/10 here.

) The (interfering) power received at BS0 from this mobile, a distance r0 from BS0 , is then given by. n PT1 r0 10z0 /10 = PR r1 r0 10(z0 z1 )/10. The number of mobiles in a code-128c for .NET differential area dA(x, y) centered at point (x, y) is dA(x, y) = 2KdA(x, y)/3 3R2 . Using this number in place of the single interfering mobile described above, integrating over all S0 , and then taking the average or expectation (denoted by the symbol E) over the random variables, one nds the total average interference power at base station BS0 due to mobiles outside cell S0 to be given by the following integral (Viterbi, 1995): r1 n (z1 z0 )/10 2K I S0 = d A(x, y) PR E 10 r0 3 3R 2.

S0 . 2K = PR E 10(z0 z1 )/10 2 3 3R S0 . r1 (x, y) r0 (x, y). d A(x, y). (6.13). The expression for the out side interference power thus breaks into the product of two terms, one purely geometric, involving an integration over the outside region S0 , the other an average over shadow-fading terms. Numerical integration over the purely geometric double integral, including the term 2/3 3R2 in the evaluation, provides a value of 0.44 for the case n = 4 (Viterbi, 1995).

(Closed-form integration using circular-cellular geometry will be shown shortly to provide a very similar value.) Consider now the shadow-fading expression. It will turn out that this term is very signi cant, producing a relatively large.

Multiple access techniques value for the model adopte d here. Hence shadow fading adds signi cantly to the interference power. (The effect of the geometric term dies out relatively quickly for the propagation parameter n = 4, so that rst-tier cells surrounding the cell S0 account for most of the interference power.

We shall demonstrate this effect directly using the circular-cell model.) We now focus on evaluation of the shadow-fading expression in (6.13).

The analysis to follow was rst described in Viterbi et al. (1994) and appears as well in Viterbi (1995). The two shadow-fading random variables z0 and z1 represent power variations as measured at the two base stations BS0 and BS1 , respectively.

Note, however, that, because the power received at these base stations is due to mobiles transmitting in the vicinity of point (x, y), shadow-fading effects in that region must be incorporated as well. Viterbi et al. (1994) and Viterbi (1995) model this by assuming that the shadow-fading random variable (rv) measured at each base station is given by the sum of two random variables.

One, common to both shadow-fading terms, represents the effect of shadow fading in the vicinity of the transmitting mobiles at (x, y). The second rv represents random power variations encountered along the propagation path and is assumed independent along the two paths from (x, y) to BS0 and BS1 . In particular, the assumption made is to write the two shadow-fading random variables z0 and z1 in the form z i = ah + bh i i = 0, 1 a 2 + b2 = 1 (6.

14). The rv h represents the sh Visual Studio .NET code128b adow fading in the vicinity of the transmitting mobile, hence common to the power received at the two base stations in question, the one, BS1 , to which directed, the other, BS0 , at which it serves as interference. The other rv, hi , i = 0 or 1, represents the added shadow fading due to the (independent) propagation conditions encountered along the two paths.

If half of the effect of the shadow fading is due to the common region about the transmitting mobile, the other half to the independence of the shadow-fading terms received at the two base stations, one must have a2 = b2 = 1/2. Note that, from this discussion, the three different random variables appearing in (6.14), h, h0 , and h1 , must individually be gaussian-distributed and independent.

Hence the rst and second moments must be related as follows E(z i ) = 0 = E(h) = E(h i ); E z i2 = 2 = E(h 2 ) = E h i2 ; E(hhi ) = 0 = E(h 0 h 1 ) Consider now the evaluation of the expectation over the shadow-fading expression appearing in (6.13). Note rst, from (6.

14), that (z 0 z 1 ) = b(h 0 h 1 ) is a gaussian rv with zero average value and variance 2b2 2 from the moment relations just tabulated. To simplify the calculation of the expectation in (6.13), de ne the variable transformation e y 10(z0 z1 )/10 (6.

15). Then y = (z 0 z 1 )/10 * loge 10 = 0.23 (z 0 z 1 ) is also gaussian, with zero average 2 value and variance y = (0.23)2 *2b2 2 = 0.

053 2 , if b2 = 1/2, as suggested above. The. Viterbi, A. J. et al.

1994 Code 128 for .NET . Soft handoff extends CDMA cell coverage and increases reverse link coverage, IEEE Journal on Selected Areas in Communications, 12, 8 (October), 1281 1288.

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