Adaptive and Iterative Signal Processing in Communications by Jinho Choi

By Jinho Choi

Adaptive sign processing (ASP) and iterative sign processing (ISP) are vital strategies in enhancing receiver functionality in communique platforms. utilizing examples from useful transceiver designs, this 2006 e-book describes the elemental concept and useful facets of either tools, delivering a hyperlink among the 2 the place attainable. the 1st components of the e-book care for ASP and ISP respectively, every one within the context of receiver layout over intersymbol interference (ISI) channels. within the 3rd half, the purposes of ASP and ISP to receiver layout in different interference-limited channels, together with CDMA and MIMO, are thought of; the writer makes an attempt to demonstrate how the 2 concepts can be utilized to unravel difficulties in channels that experience inherent uncertainty. Containing illustrations and labored examples, this e-book is appropriate for graduate scholars and researchers in electric engineering, in addition to practitioners within the telecommunications undefined.

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Extra resources for Adaptive and Iterative Signal Processing in Communications

Example text

2μλm If we let μ = 1/λmax from Eq. 44), the time constant is given and bounded as follows: τm λmax λmax ≤ . 53) Since the rate of convergence is decided by the slowest eigenmode, the largest time constant τmax = λmax /2λmin decides the overall rate of convergence. The rate of convergence becomes faster as τmax becomes smaller. Thus, the smaller the eigenspread λmax /λmin of Ry , the faster the convergence. This is the same result as in Eq. 51). Consequently, we can see that the eigenspread of Ry plays a key role in deciding the rate of convergence of the SD algorithm.

2 MMSE DFE Since the ZF DFE only attempts to remove the ISI, the noise can be enhanced. To avoid this, it is desirable to consider the MMSE criterion. With the equalizer output, dl , that estimates the desired symbol sl = bl−m¯ , the MSE is given by MSE = E[|sl − dl |2 ] = E[|bl−m¯ − dl |2 ] ⎡ = E ⎣ bl−m¯ − gm yl−m − m=0 2 N −1 M−1 f m bˆ l−m ⎤ ⎦. 23) ¯ m=m+1 Let f = [ f m+1 ¯ ˆ ˆbl−m−1 = [bl−m−1 ¯ ¯ f m+2 ¯ ˆbl−m−2 ¯ ··· ··· f N −1 ]T , bˆ l−N +1 ]T . Then, it follows that T f MSE = E bl−m¯ − ylT g − bˆ l− ¯ m−1 =E bl−m¯ − ylT T bˆ l− ¯ m−1 2 g −f 2 .

35) works with a simple example. Consider the MSE for M = 1: MSE(g) = σb2 − 2r y,s g + g 2r y , where r y = Ry , r y,s = ry,s , and g = g for M = 1. Clearly, the MSE(g) is a quadratic function of a scalar coefficient g. The gradient can be written as follows: d MSE(g) = −2r y,s + 2r y g. dg d MSE(g) > 0 and a new g should be smaller than If g > gmmse = r y,s /r y , we can see that dg the current g as given in Eq. 35) to approach gmmse . An illustration of the MSE function and its derivative is shown in Fig.

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