ECE255C: Network Information Theory



Basic probability. ECE255A recommended but not required. Those who have not taken ECE255A/ECE255B, however, ought to fill in the pre-authorization form.

Course description

Network information theory studies the fundamental limits on information flow in networks and optimal coding techniques and protocols that achieve these limits. It extends Shannon's point-to-point information theory and the Ford–Fulkerson max-flow min-cut theorem to networks with multiple sources and destinations, broadcasting, interference, relaying, distributed compression, and computing. Although a complete theory is yet to be developed, several beautiful results and techniques have been developed over the past forty years with applications in wireless communication, the Internet, and other networked systems.

This course aims to provide a broad coverage of key results in network information theory and their applications in wireless communications, randomness generation, content broadcasting, and machine learning. Topics include background (information measures and typical sequences, point-to-point communication), basic network information theory (multiple access channels, broadcast channels, Slepian-Wolf coding, and Gray-Wyner system), wireless communications (interference channels and cloud radio access networks), randomness generation (source and channel simulation), index and network coding, and applications in machine learning.

Course requirements and grading


MW 11:00 am–12:20 pm, Jacobs Hall 2315

Teaching staff