Course Handouts: Advanced Digital Communications
This class will mainly follow the lecture notes of Prof. Ruediger Urbanke and Prof. Suhas Diggavi.
The review part will follow the lecture notes from the pre-requisite Principles of Digital Communications (PDC), taught in the summer semester by Prof. Bixio Rimoldi.
Attention!
The lecture notes bellow are just for reference. Sometimes we deviate from them and use other approaches in deriving and proving various concepts. Notations might differ as well. Printed notes might contain typos as well.
The main notes for this class are those given during the class time. (developed on the blackboard).
Lecture notes of Prof. Urbanke
Lecture notes of Prof. Diggavi
Lecture notes of Prof. Cioffi
* Week 17.09 - 21.09: Review from PDC: Hypothesis testing, Examples (Vector Gaussian Channel), Gaussian vectors, Inner product spaces transforms (with Gram-Schmidt procedure), Sampling theorem, Nyquist criterion. Follows the lecture notes of PDC.
* Week 24.09 - 28.09: Review from PDC: Complex Gaussian random variables, Passband systems (equivalent channel and noise model). Follows the lecture notes of PDC.
* Week 01.10 - 05.10: Finish with Complex Gaussian random variables and the baseband equivalent noise model. Lecture notes: PDC (will be available in class). Start with studying the Transmission over Linear Time-Invariant channels. We plan to follow the lecture notes of prof. Diggavi (Chapter 4).
* Week 08.10 - 12.10: Inter-Symbol Interference (ISI) channels. Noise whitening. Maximum Likelihood Sequence Estimation (MLSE). Viterbi algorithm. BCJR algorithm. (Chapter 4, prof. Diggavi notes). Review of Z-transform (prof. Urbanke notes, pp. 20-22, 42-45).
* Week 15.10 - 19.10: Maximum Likelihood Sequence Estimation (MLSE). Viterbi algorithm. BCJR algorithm. (Chapter 4, prof. Diggavi notes). Equalization: Low complexity suboptimal receivers. (Chapter 5, prof. Diggavi notes).
* Week 22.10 - 26.10: Linear Estimation. Orthogonality principle. Smoothing. Linear Prediction. Equalization. (Chapter 5, prof. Diggavi notes).
* Week 29.10 - 02.11: Linear Prediction. Suboptimal detection: Equalization. Zero-forcing equalizer (ZFE). MMSE linear equalization (MMSE-LE). (Chapter 5, prof. Diggavi notes).
* Week 05.11 - 09.11: Decision-feedback equalizer (DFE) (Chaper 5.5, prof. Diggavi notes). Transmission structures. Multicarrier Transmission (OFDM). (Chapter 6.2, prof. Diggavi notes).
* Week 12.11 - 16.11: Tomlinson-Harashima precoding. Multicarrier Transmission (OFDM). (Chapter 6, prof. Diggavi notes). Midterm exam.
* Week 19.11 - 23.11: Multicarrier Transmission (OFDM). (Chapter 6, prof. Diggavi notes).
* Week 26.11 - 30.11: OFDM. Channel Estimation. (Chapter 6, prof. Diggavi notes). Wireless Channel Models (Chapter 7, prof. Diggavi notes).
* Week 03.12 - 07.12: Wireless channel models (Chapter 7, prof. Diggavi notes). Detection for wireless channels. Diversity methods in wireless communications (Chapter 8, prof. Diggavi notes).
* Week 10.12 - 14.12: Detection for wireless channels. Diversity methods in wireless communications (Chapter 8, prof. Diggavi notes).
* Week 17.12 - 21.12: Frequency Diversity. Spatial Diversity. (Chapter 8, prof. Diggavi notes). Multi-user communications (Chapter 9, prof. Diggavi notes).