By Jinho Choi

ISBN-10: 0521864860

ISBN-13: 9780521864862

Adaptive sign processing (ASP) and iterative sign processing (ISP) are very important options in bettering receiver functionality in communique structures. utilizing examples from functional transceiver designs, this 2006 e-book describes the elemental conception and sensible points of either tools, supplying a hyperlink among the 2 the place attainable. the 1st components of the e-book take care of ASP and ISP respectively, each one within the context of receiver layout over intersymbol interference (ISI) channels. within the 3rd half, the functions 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 thoughts 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 info for Adaptive and Iterative Signal Processing in Communications

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5 Adaptive decision feedback equalizers This recursion is called the RLS algorithm since it solves the exponentially weighted SSE recursively. The only difference from the LMS algorithm is the updating vector. 68a) . 68b) −1 The updating vector is proportional to yl in the LMS algorithm, and proportional to Σl−1 yl in the RLS algorithm. In the RLS algorithm, we need to compute the inverse of Σl . The matrix inversion requires the complexity of order O(M 3 ). Since the matrix inversion shall be performed for every received signal sample, yl , it is obviously prohibitive.

Bit error rate performance of the MMSE LE for Channel A and Channel B. 7. Structure of the DFE. there are frequency nulls, as shown in Eq. 14), the corresponding frequency responses of the MMSE LE become zero to suppress the noise since there is no useful signal. Generally, if there are more frequency nulls, more corresponding frequency responses of the MMSE LE become zero and this results in a worse performance of Channel A, as shown in Fig. 6. 3 Decision feedback equalizers Depending on the channel spectrum, the performance of the LE varies.

0 0 .. 3 Decision feedback equalizers where H is the channel filtering matrix and g is the equalization vector for FFF given by g = [g0 g1 · · · g M−1 ] . Note that the length of {cm } is N = M + P − 1. The output of the FFF is given by ql = gl ∗ (h l ∗ bl + n l ) = N −1 M−1 cm bl−m + m=0 gm n l−m . m=0 Consider the signal part only and assume that the DFE is to estimate bl−m¯ at time l, where m¯ is a decision delay. The signal part can be rewritten as follows: N −1 N −1 m¯ cm bl−m = m=0 cm bl−m + m=0 cm bl−m .

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Adaptive and Iterative Signal Processing in Communications by Jinho Choi

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