EECE 564

Detection and Estimation of Signals in Noise

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.

3 credits

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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