Maximum Likelihood Sequence Detection - spsc

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Discrete-Time Equivalent Channel Model for PAM ∞ jωT ⎡ ⎛ 2π ⎞⎤ ⎡ ⎛ 2π ⎞⎤ ⎡ ⎛ 2π ⎞⎤ P( e ) = ∑ G⎢ j⎜ω− m⎟ BE j⎜ω− m⎟ F j⎜ω− m⎟ m=−∞ T ⎥ ⎢ T ⎥ ⎢ T ⎥ ⎣ ⎝ ⎠⎦ ⎣ ⎝ ⎠⎦ ⎣ ⎝ ⎠⎦ 2N 0 ( j T ∞ ω SZ e ) = ∑ F j + m T m=−∞ ( ( 2 π ω )) 2 T SPSC 4
Matched Filter as Receiver Front End (1) • matched filter as receive filter • discrete-time equivalent channel model SPSC 5
Page 1 and 2: Maximum Likelihood Sequence Detecti
Page 3: Channel Model SPSC 3
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 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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