Digital Communications Systems
Specific Course Information:
2021-2022 Catalog Data: The purpose of the course is to give students a comprehensive introduction to digital communication principles. The major part of the course is devoted to studying how to translate information into a digital signal to be transmitted, and how to retrieve the information back from the received signal in the presence of noise and intersymbol interference (ISI). Various digital modulation schemes are discussed through the concept of signal space. Analytical and simulation models for digital modulation systems are designed and implemented in the presence of noise and ISI. Optimal receiver models for digital base-band and band-pass modulation schemes are covered in detail.
Specific Goals for the Course:
Outcomes of Instruction: By the end of this course the student will be able to:
- An ability to apply knowledge of mathematics, science, and engineering.
- An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability.
- An ability to identify, formulate, and solve engineering problems.
- An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
Brief list of topics to be covered:
- Review of mathematical tools: Orthogonal functions, probability theory, random processes, Markov processes.
- Information theory: Information measures (self-information, mutual information, channel capacity), looseless source coding, Huffman codes, channel coding, Shannon coding theorems.
- Representation of band-pass signals and systems: Band-pass signals and noise representation (Hilbert transform); signal space representation.
- Digital modulation schemes: Memoryless digital modulation methods (ASK, PSK, FSK, QPSK), modulation with memory (base-band and band-pass), spectra of digitally modulated signals.
- Optimum receivers for additive white Gaussian noise (AWGN) channel: Maximum a posteriori and maximum likelihood detection, matched filter demodulation, sequence detectors, symbol by symbol MAP detector for channels with memory, receiver performance.
- Error control coding fundamentals: Finite fields, generator and parity check matrices block and convolutional codes and their decoders, Hamming codes, syndrome decoding, iterative decoders.
Relationship to Student Outcomes
ECE 435A contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:
1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.