Shannon's source-channel separation theorem states that separating the source coder (compressor/decompressor) and the channel coder (error correction coder/decoder) via the universal digital interface of 'bits' is optimal for point-to-point communication of a single data source. While this modular design principle has inspired the basic architecture for most of today's communication systems, it is outperformed by more complex 'joint' source-channel coders for communication of multiple sources over networks---an important task in today's explosive demand and supply of distributed information sources. At the same time, many emerging applications involve computing a summary of data from multiple nodes, making a coordinated decision, and performing a joint action among these nodes, rather than merely communicating sources. This research establishes a hybrid source-channel coding architecture for these applications that is as simple as Shannon's separation architecture, yet achieves much improved performance. The new architecture will eventually lead to the discovery of practical algorithms for distributed computation, sensing, decision making, and coordination over networks.
Specifically, this research focuses on and develops new approaches for tackling the following problems: 1) hybrid coding for communicating correlated sources over multiuser channels (a unified approach for joint source-channel coding), 2) hybrid coding for network communication (a new relaying scheme based on joint source-channel coding), 3) implementation issues for hybrid coding (design of a practical code that is a good channel code and a good source code simultaneously), and 4) a mathematical framework for performance analysis and code design (information theoretic tools when the codebook and the message are entangled).