Detection and Estimation of Signals in Noise

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3 Credits

EECE 564

Formulation of the detection problem, optimum receiver principles, signal space, maximum likelihood decisions, error performance calculations. Estimation of signals in noise, linear and non-linear estimation, cost functions, recursive mean square estimation, Wiener and Kalman filters.

Course Outline

1. Basic Elements of a Digital Communication System

  •   Transmitter
  •   Receiver
  •   Communication channels
  •   What problem do we try to solve?

2. The Probability and Stochastic Processes

  •   Review of probability (random variables, probability density functions, Chernoff bound, central limit theorem)
  •   Review of stochastic processes (statistical averages, power density spectrum, system responses)

3. Characterization of Communication Signals and Systems

  •   Equivalent complex baseband representation of bandpass signals and systems
  •   Signal space representation of signals
  •   Linear modulation schemes (PAM, PSK, QAM, differential PSK)
  •   Nonlinear modulation schemes - Continuous phase modulation (CPM)

4. Optimum Reception in Additive White Gaussian Noise (AWGN)

  •   Optimum coherent receivers (demodulation, detection, maximum-likelihood, maximum-a-posteriori)
  •   Performance analysis of optimum receivers
  •   Optimum and suboptimum noncoherent receivers (differential detection, multiple-symbol differential detection)

5. Signal Design for Bandlimited Channels

  •   Characterization of bandlimited channels
  •   Signal design (Nyquist criterion)

6. Equalization of Channels with ISI

  •   Maximum-likelihood sequence estimation (MLSE)
  •   Linear equalization (LE)
  •   Decision-feedback equalization (DFE)
     
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