52 we get e notice that e e 453 454

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Unformatted text preview: . Notice that and Ê´Ã · ½µ Ê´Ã µ · Áà ·½ÁÌ ·½ à (4.40) ´Ã · ½µ ´Ã µ · Áà ·½Áà ·½ (4.41) Using the matrix inversion lemma5, we get Ê ½ ´Ã · ½µ Ê ½ ´Ã µÁà ·½ÁÌ ·½Ê ½ ´Ã µ Ã Ì Ê ½ ´Ã µÁ ½ · Áà ·½ à ·½ Ì ´Ã µ · Áà ·½ Áà ·½ ´Ã µ Ê ½´Ã µÁà ·½ ½ · ÁÌ ·½Ê ½´Ã µÁà ·½ Ã Ê ½ ´Ã µ ´Ã · ½µ (4.42) (4.43) The procedure is called the recursive least squares (RLS) algorithm. In many cases, the RLS algorithm converges much faster than the steepest descent algorithm at the expense of more complex computation. 4.3.5 Decision feedback equalizer Recall from the equivalent discrete-time model in Figure 4.14 that Á ¼· Á Á ·Ò (4.44) The current symbol we want to determine is Á . If we had known the other symbols exactly, an obvious approach to eliminate ISI would be to subtract their effects off6 , i.e., the equalizer would give Á Á Á (4.45) In general, we do not know all the symbols that are affecting the reception of the current symbol. However, it is possible to use...
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This note was uploaded on 12/13/2012 for the course EEL 6535 taught by Professor Shea during the Spring '08 term at University of Florida.

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