EE307

Description: This course covers the fundamentals of data communication. Specifically, this course explores design principles and performance considerations for communication systems, and provides insight into design challenges for next-generation communication systems and data networks. This course provides hands-on experience with Software-Defined Radios (SDRs).

Course objectives: When a student completes this course, s/he should be able to:

1. Describe the goal, functionality, and trade-offs of the main components of a digital communication system.

2. Examine a communication system using concepts from Sampling Theory, Fourier Analysis, Information Theory and Quantization.

3. Describe solutions to the different impairments caused by the communication medium.

4. Employ the Software-Defined Radio utilized in class to implement a simple wireless communication system.

5. Describe techniques utilized for allowing multiple users to access a common communication medium.

Course topics:

• Fourier Transform and Sampling Theorem
• Quantization and Optimal Quantizers
• Measure of Information (Entropy)
• Source Coding
• Digital Modulation 1D and 2D
• Geometric Signal Representation
• Signal Reception in Noise
• Bit Error Rate (BER) Analysis
• Link Budget
• Channel Capacity and Channel Coding
• Data Link Layer and Error Correction
• Data Networks, ALOHAnet, and WiFi

Labs: The course will feature laboratory experiments using LabVIEW and SDRs. Laboratory experiments include:

• Lab 1: Introduction to LabVIEW and to the SDR
• Lab 2: Simple PAM-2 transmitter and receiver design
• Lab 3: Complete QAM-M system and BER analysis
• Lab 4: Effects of the wireless channel and countermeasures
• Lab 5: Introduction to Multiple Access techniques

References:

• John G. Proakis and Masoud Salehi. Communication Systems Engineering. Second Upper Saddle River, NJ, USA: Prentice-Hall, 2001
• Dimitri P. Bertsekas and Robert G. Gallager. Data Networks. Prentice-Hall, 1992
• Simon Haykin. Communication Systems. Wiley Publishing, 2009
• Mor Harchol-Balter. Introduction to Probability for Computing. Cambridge: Cambridge University Press, 2023
• Thomas M. Cover and Joy A. Thomas. Elements of Information Theory. Wiley, 2005

Acknowledgement: the structure of this course was inspired both by the previous version of EE307 taught by Prof. Michael Honig and the MIT course 16.36 Communication Systems and Networks taught by Prof. Eytan Modiano. The SDR laboratory was funded by a Walter P. Murphy Society Award and by the Department of Electrical and Computer Engineering.