DIGITAL COMMUNICATIONS

Every class = 2 hours

CLASS 1: Introduction. Transmission systems with memory: Model for signals with finite memory.

CLASS 2: Coded linear modulations with long shaping pulses interpreted as signals with finite memory. Examples.

CLASS 3: Excercises (chapter 1).

CLASS 4: MAP sequence and symbol detection strategies: introduction.

CLASS 5: MAP sequence detection strategy for the general case of signals with finite memory. Viterbi algorithm.

CLASS 6: Implementation aspects of the Viterbi algorithm. MAP sequence detection strategy for coded linear modulations: Ungerboeck observation model.

CLASS 7: Ungerboeck observation model: expression of the branch metrics, special cases, condition for the absence of ISI.

CLASS 8: MAP sequence detection strategy for coded linear modulations: Forney observation model.

CLASS 9: MAP sequence detection strategy for coded linear modulations: introduction to the performance analysis. Upper bounds for the symbol error probability of a MAP sequence detector.

CLASS 10: Pairwise error probability and its computation in the case of the AWGN channel. Distance spectrum. Computation of the distance for the case of coded linear modulations and the Ungerboeck model.

CLASS 11: Computation of the distance for the case of coded linear modulations and the Forney model. Exercises (chapter 2).

CLASS 12: Exercises (chapter 2).

CLASS 13: Introduction to the problem of sequence detection in the presence of unknown parameters. Sequence detection in the presence of unknown parameters: channel models, sufficient statistics, optimal strategy in the presence of an unknown stochastic parameter.

CLASS 14: Sequence detection in the presence of unknown parameters: memory truncation. Examples: noncoherent sequence detection and receivers based on linear prediction. UMP test. Generalized likelihood.

CLASS 15: Approach based on estimation. Estimators' classifications. CRB and MCRB. DA estimation. Example: Rife & Boorstyn algorithm and related MCRB.

CLASS 16: 1st order PLL and its analysis.

CLASS 17: Decision-directed PLL, S-curve of a DD PLL. Per-survivor processing and tentative decisions. NDA estimation.

CLASS 18: Soft-decision-directed estimation. Sufficient statistics for channels with unknown parameters.

CLASS 19: Exercises (chapter 3).

CLASS 20: Continuous phase modulations.

CLASS 21: Trellis coded modulations.

CLASS 22: Trellis coded modulations: example. Reduced-state sequence detection.

CLASS 23: Adaptive equalization.

CLASS 24: Channel identification. Excercises (chapter 4).

CLASS 25: MAP symbol detection: the BCJR algorithm. Implementation of the BCJR algorithm in the logarithmic domain. Soft-output Viterbi decoder.

CLASS 26: The computation of the information rate. Mismatched detection. Pragmatic capacity.

CLASS 27: Turbo codes and iterative decoding.

CLASS 28: EXIT charts.

CLASS 29: Excercises (chapter 5 and 6).

CLASS 30: Factor graphs (FGs) and the Sum-Product Algorithm (SPA). FG transformations.

CLASS 31: Low density parity check codes and their decoding. BCJR and Viterbi algorithms derived through FG/SPA.

CLASS 32: Application of the FG/SPA framework to detection and estimation problems.

CLASS 33: Excercises (chapter 7)

CLASS 34: Codes for fading channels. Bit-interleaved coded modulation.

CLASS 35: Space-time (ST) codeword design criteria for fading channels. ST block codes. The Alamouti scheme.

CLASS 36: ST trellis codes. Layered ST codes.