Maximum Likelihood Sequence Detection - spsc

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Matched Filter as Receiver Front End (2) • autocorrelation of baseband receive pulse shape h(t) ρ ∞ * h( ) = ∫ ( ) ( − ) −∞ k h t h t kT dt • folded spectrum ∞ ∞ jωT jωkT 1 S ( e ) = ∑ ρ ( k) e = ∑ T H j + m ( ( 2 π ω )) 2 h h T k=−∞ m=−∞ SPSC 6
Whitening of the Matched Filter (1) • matched filter colored noise jωT S ( e ) = 2 N S ( e ) • spectral factorization Z 2 0 h jωT Sh ( z ) = γ M ( z ) M (1/ z ) • minimal phase and allpass system S ( z) = H ( z) H ( z) h min ∗ ap ∗ SPSC 7
Page 1 and 2: Maximum Likelihood Sequence Detecti
Page 3 and 4: Channel Model SPSC 3
Page 5: Matched Filter as Receiver Front En
Page 9 and 10: Detection • ML Detection of a Sin
Page 11 and 12: ML Detection of a Single Symbol •
Page 13 and 14: ML Detection With Intersymbol Inter
Page 15 and 16: ML Detection With Intersymbol Inter
Page 17 and 18: Sequence Detection • Markov Chain
Page 19 and 20: Markov Chains (2) • Trellis diagr
Page 21 and 22: • ISI Model Markov Chain Signal G
Page 23 and 24: The Viterbi Algorithm (1) • A. Vi
Page 25 and 26: The Viterbi Algorithm (3) • Survi
Page 27 and 28: The Viterbi Algorithm Example (2) S
Page 29 and 30: Error Probability Calculation • E
Page 31 and 32: Detection Error Pr[detection error]
Page 33 and 34: Upper Bound of Detection Error (2)
Page 35 and 36: Lower Bound of Detection Error (2)
Page 37: Symbol Error Probability (2) • On

Maximum Likelihood Sequence Detection - spsc

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