Maximum Likelihood Sequence Detection - spsc

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The Viterbi Algorithm (2) • <strong>Sequence</strong> of inputs = path through the trellis • Assign branch metric = y −s • Path metric = Σ branch metrics • Choose lowest path metric = minimize y− sˆ k k 2 SPSC 24
The Viterbi Algorithm (3) • Survivor path of k-1 = smallest path metric to node k-1 • Only hold survivor path • For node k choose smallest branch metric + survivor path of k+1 SPSC 25
Page 1 and 2: Maximum Likelihood Sequence Detecti
Page 3 and 4: Channel Model SPSC 3
Page 5 and 6: Matched Filter as Receiver Front En
Page 7 and 8: Whitening of the Matched Filter (1)
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: The Viterbi Algorithm (1) • A. Vi
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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