In the modeling and performance characterization of communication channels, the mathematical framework of information theory has contributed significantly to both conceptual understanding as well as technology development. Despite its magnificent success for certain applications, there are many applications, particularly in delay-constrained and many network scenarios, in which classical > information theory has offered fewer insights, or otherwise the available insights have not been fully integrated into existing systems. One of the drawbacks of the classical theory is its reliance on long coding blocklengths. We believe that application of information theory to modern applications can be accelerated by a "finite blocklength" information theory that is general enough to handle a wide class of important channel models but not so complex and subtle so as to be essentially unusable by system designers. This research centers around activities for developing a general framework of finite blocklength information theory for modern communication systems and networks. The guiding objective is to develop suitable abstractions of communication channels and coding strategies in terms of a single random variable called the mutual information density rate, rather than its expectation. The anticipated abstractions will enable characterization of performance and architectural evaluation by network designers as well as facilitate orders of magnitude speedups in network simulation. The technical approach is comprehensive, leveraging theoretical analysis, computer simulation, and testbed experiments. Opportunities for integration of research and education have been identified, and substantial cross-fertilization is expected.
