Maximum Likelihood Sequence Detection - spsc

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ML <strong>Detection</strong> of a Signal Vector • Vector of Symbols • Maximize f ˆ ˆ ˆ ˆ YS | ( y | s) = fNS | ( y − s| s) = fN( y −s) • Equivalent to maximize 1 1 f ( y ˆ ⎛ − s ) exp ˆ N = M /2 M ⎜− − 2 (2 π) σ ⎝ 2σ y s 2 ⎞ ⎟ ⎠ • Equivalent to minimizing y − sˆ SPSC 12
ML <strong>Detection</strong> With Intersymbol Interference (1) • Generator is LTI filter • Input single data symbol A • Model h ,0≤ k ≤ M k Y= hA + N SPSC 13
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: ML Detection of a Single Symbol •
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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