EE179 - Introduction to Communications -
Winter 2007-2008
This class will provide a basic introduction to communication systems
and techniques. Specific topics covered in the class include Fourier
techniques and their use in communication system analysis, energy and
power spectral density, probability and random processes/signals,
amplitude modulation (AM), frequency modulation (FM) digital modulation,
noise in communication systems, overview of current systems: the
public-switched telephone network, radio and TV broadcasting, cellular
and cordless telephones, satellite communications and paging.
Basic Course Information
Class Time and Location:
MWF 11-11:50, Hewlett 101.
Instructor: Robert M. Gray, Packard 261, rmgray at stanford, 3-4001.
Office Hours: M 12-1 W 2:30-3:30 pm and by appointment.
Teaching Assistant: Tosin Olatunbosun, olutosin@stanford.edu.
Office Hours: TW 4:00-6:00 pm, Packard 109.
Administrator: Kelly Yilmaz,
259 Packard, kelly.yilmaz@stanford, 3-34539.
Homework pickup/dropoff: Th 11 am-12 pm.
Prerequisites: EE102a (or equivalent).
Grading: Homeworks: 30%, Midterm Exam: 30%, Final Exam: 40%.
Homeworks can be collaborative.
Detailed Course Information
Announcements
This course is usually taught
by Professor Andrea Goldsmith. Professor Gray is teaching it during her
sabbatical and will follow her notes and instructional materials.
Homework
Lectures
Slides are pdf files, four slides to a page.
Lecture 1: Introduction and Communication Systems Today
Lecture 2: Fundamentals of Communication Systems
Lecture 3: Fundamentals of Communication Systems.
Lecture 4: Fourier Series and Transforms Review.
Lecture 5: Fourier Transform Review.
Lecture 6: Special Functions, Sampling,
Filtering, Distortion, and Equalization.
Lecture 7: Energy and Power Spectral Density.
Lecture 8: Properties of PSD, Periodic and Random Signal PSD
and Autocorrelation.
Lecture 9: Probability and Random Variables.
Lecture 10: Conditional Probability, Independence, Random Variables,
expectations.
Lecture 11: Multiple Random Variables.
Lecture 12: Multiple Random Variables. Gaussian Random Variables.
CLT.
Lecture 13: Random Processes. Stationarity.
Lecture 14: Mean and Autocorrelation. WSS processes,
PSD
Lecture 15: PSD Properties, Filtering/Modulation,
Gaussian Processes, white noise.
Lecture 16: Midterm Coverage, More details on
Filtering/Modulation and Gaussian processes. Examples
Lecture 17: Introduction to Modulation, AM Modulation,
Generation/Detection of AM Signals
Lecture 18:
DSBSC, Costas Loop.
Lecture 19: Noise in AM, SSB/VSB.
DSBSC.
Supplementary topic: Single Sideband and Narrowband Gaussian Noise and Noise in SSB
Lecture 20: AM Radio.
Superheterodyne Receivers. Frequency Modulation.
Lecture 21: FM Spectral Analysis, Modulation, and Detection.
Lecture 22: WBFM Modulation, FM Detection, Digital Modulation,
ASK/PSK/FSK.
Lecture 23: Digital Demodulation. Error Probability in ASK/PSK/FSK.
Lecture 24: Detection and Error Probability ASK/PSK/FSK.
Lecture 25: Course Summary