Lecture Notes

In the table below, for each class you find the topic of the lecture;
the topics are linked to a set of slides covered in the class.
Slides are not available for all lectures.

The final column may include links to other resources from the lecture, e.g., MATLAB files.

Date | Topic | Other Resources |
---|---|---|

Jan. 26 | Introduction | Textbook: chapter 1 |

Feb. 2 | Mathematical Prerequisites: Working with Gaussian random variables and vectors; Introduction to random processes |
Textbook: Appendix A and section 3.1 |

Feb. 9 | Mathematical Prerequisites: Random processes: stationarity; white Gaussian noise; filtering of Random processes. |
Textbook: Appendix A and section 3.1 |

Feb. 16 | Mathematical Prerequisites: Random processes: power spectral density; Introduction to linear vector spaces: norms and inner products, Hilbert spaces, subspaces, projection theorem. |
Textbook: Appendix A and section 3.3 |

Feb. 23 | Mathematical Prerequisites: Introduction to linear vector spaces: representation in terms of bases, orthonormal bases, Gram-Schmidt procedure; representation of random processes: Karhunen-Loeve expansion. |
Textbook: Appendix A and section 3.3 |

March 2 | Optimum Receiver Principles: Introduction: computing the probability of error for a simple communication system. |
Textbook: sections 3.4 and 3.5 |

March 9 | Optimum Receiver Principles: Statistical Hypothesis Testing. |
Textbook: sections 3.4 and 3.5 |

March 23 | Optimum Receiver Principles: Optimal receiver front-end; matched filter receiver. |
Textbook: sections 3.4 and 3.5 |

April 6 | Optimum Receiver Principles: suboptimal receiver frontends. Introduction to M-ary signal sets. |
Textbook: sections 3.4 and 3.5 |

April 13 | Optimum Receiver Principles: Computing the probability of error for M-ary signal sets; union bound, nearest neighbor approximation, energy efficiency. | Textbook: sections 3.5 and 3.6 |