Herself’s Artificial Intelligence

If you are interested in network information theory you might want to check out this pdf of combined lecture notes from several graduate classes.

Network information theory deals with 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 potential applications in wireless communication, the Internet, and other networked systems.
This set of lecture notes, which is a much expanded version of lecture notes used in graduate courses over the past eight years at Stanford, UCSD, CUHK, UC Berkeley, and EPFL, aims to provide a broad coverage of key results, techniques, and open problems in network information theory. The lectures are organized in a “top-down” manner into four parts: background, single-hop networks, multi-hop networks, and extensions. The organization attempts to balance the introduction of new techniques and new models. Extensions (if any) to many users and large networks are discussed throughout. The lectures notes provide a unified, simplified, and formalized treatment of achievability using a few basic lemmas. The proofs in the lecture notes use elementary tools and techniques, which should make them accessible to graduate students in EE, CS, Statistics, and related fields as well as to researchers and practitioners in industry.

Network Information Theory Notes

pdf only