HBBCL Network Information Theory Research

Overview of Network Information Theory at Harvard:

Network information theory is the study of reliable communication in a network setting, where there are many sources and users who wish to communicate with one another. The internet, wireless networks, and other communication systems of the future allows the connectivity between many systems and users. Therefore it is of critical importance to study the interaction and behavior of interconnected systems.

In such large scale systems, distributed control is important to enable reliable communication in a multitude of configurations. In this respect Harvard has done some pioneering work in understanding the capacity and coding of systems with feedback, and finite state channels.

Our effort has been divided into three main themes:

Feedback Capacity and Feedback Codes:

In a large scale communication network, feedback about the condition of the channel may be available to each user. It is known that for channels with memory, feedback can increase the channel capacity. We have studied the feedback and regular feedforward capacities of certain channels, and constructed codes for them.

For more details, see the publication page.

Distributed Signal Processing:

Sensor networks have to be able to process signals in a distributed manner. Many evolving low-power sensor network scenarios need to have high spatial density to enable reliable operation in the face of component node failures as well as to facilitate high spatial localization of events of interest. This induces a high level of network data redundancy, where spatially proximal sensor readings are highly correlated. We have developed a new way of removing this redundancy is a completely distributed manner, using distributed source coding methods.

For more details, see the following publication.