The basic point of section 22 is that if in our

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Unformatted text preview: de decoding. Reduced State Sequence Estimation (RSSE) approaches, which have recently be proposed for large-state problems, can also be effective. Kevin Buckley - 2007 11 2.5 Symbol-by-Symbol MAP This Subsection is an extension of the discussion in Subsection 5.1.5 of the Course Text. In Sections 2.2 and 2.3 of the Course, we described symbol detection and sequence estimation, respectively. The basic point of Section 2.2 is that if, in our sampled matched filter output observation, there is no symbol memory, optimum processing is symbol-by-symbol decoupled. We can optimally estimate one symbol at a time using only the observation over that symbol time. In Section 2.3 we saw that if there is symbol memory in our observation, MLSE requires joint processing of all the data to optimally estimate the symbol sequence. In understanding the significance of this current Section, the key point is that MLSE does optimum sequence estimation. What if we want optimum symbol estimation when our observation has symbol memory? This is the question we address below. In this Section we discuss what is known in t...
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