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the modem mode according to the channel quality in order to maintain a certain target bit error rate and an improved bits per symbol throughput performance. The RBF based equalizer is introduced a wideband Adaptive Quadrature Amplitude Modulain tion (AQAM) schemein order to mitigate the effects the dispersive multipath fading of channel. We introduce the short-term Error Rate (BER) the channel quality Bit as measure. Lastly, a comparative study is conducted between the constituentfixed mode, the conventional DFE based AQAM scheme and the RBF based AQAM scheme in terms of their BER and throughput performance.

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I tried very hard to make sure everything in this book is 100 percent accurate. If you notice any errors or omissions, please let me know by emailing me at That way, I can x them in future editions.

b(1) b(2) . . . b(Q)

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PID The process identi cation for the application instance File The le being read or written Read The current speed (in bytes per minute) at which the le is being ...

Figure 8.3 shows the 8 possible noiseless channel states VI, for a BPSK modem and the noisy channel output vk in the presence of zero mean AWGN with variance 0; = 0.05. It is forms clusters and the centroids of these clusters are the seen that the observation vector VI, noiseless channel states rz.The equalization problem hence involves identifying the regions within the observation space spanned by the noisy channel output v k that correspond to the transmitted symbol of either II,= +l or 1, = -1. A linear equalizer performs the classificationconjunction with a decision device, which in is often a simple sign function. The decision boundary, as seen in Figure 8.3, is constituted as it is by the locus of all values of vk, where the output of the linear equalizer is zero demonstrated below. For example, for a two tap linear equalizer having tap coefficients ( - 1 and Q,at the decision boundary we have:

Cell D8 contains the first exponential smoothing formula,

= = = = =

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assuming that the $fruit_basket variable was unbound at the first assignment. The same effect could also have been accomplished by omitting the indices in the assignment, like so:

When you have made the changes to the Onion Skin Options, click OK to close the dialog box. The Onion Skins are automatically enabled with the setting you specified. You can toggle the Onion Skins on and off by using the Onion Skin icon at the bottom of the Animation panel. As you move along in your animation, the Onion Skins move with you, helping you to create any frame along the timeline. You can change the settings at any time as your needs change.

where x(k) = [x1 (k), . . . , xm (k)]T is a noisy sensor vector, s(k) = [s1 (k), . . . , sn (k)]T is a source signal vector, (k) = [ 1 (k), . . . , m (k)]T is the noise vector, and H is an unknown full rank m n mixing matrix. It is assumed that only the sensor vector x(k) is available. The objective is to design a feed-forward or recurrent neural network and an associated adaptive learning algorithm that enables estimation of sources and/or identi cation of the mixing matrix H and/or separating matrix W with good tracking abilities for time variable systems. The present section is devoted to the analysis of learning algorithms for a typical but simple instantaneous blind source separation problem. Here, we assume that the number of source signals is known and is equal to the number of sensors (with m = n), so that both H and W are nonsingular n n matrices. The source signals si (k) are assumed to be mutually independent with zero-mean (E{si (k)} = 0). We also assume that additive noise terms (k) are negligible or reduced to be at negligible levels by the preprocessing stated in the previous chapters. The following section gives a prototype of mathematical analysis. We will then relax most of these constraints in later sections. 6.1.1 Kullback Leibler Divergence - Relative Entropy as Measure of Stochastic Independence

e ( n B 1) e(nB)

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