1. The Interaction between Communication, Computation, and Control Sekhar Tatikonda, Yale University
Coordination and computation over wireless networks
Traditionally the design has been separated
Projects:
Role of feedback in communication
Control with communication constraints
Distributed detection and compression in sensor networks
Advanced iterative decoding techniques

2. Feedback in Communication
Traditional information theory does not treat latency
Examples:
- streaming video
- communication in the control loop
Acks and channel measurements can increase capacity
Output feedback can decrease latency:
- Error decays at a double exponential rate over fading channels
Goals:
- develop efficient feedback codes
- practical issues: noisy feedback, what do we feedback

3. Control with Communication Constraints
Controls: x := Ax + Bu + w
Consider communication between sensors and controller and between controller and actuators
The communication requirements for stability:
C > log (det A)
Similar results for other performance objectives
Goals:
- Communication coordination between distributed controllers
plant
controller

4. Distributed Processing in Sensor Networks
Sensor measurements are correlated and localized
Low power constraints
Minimize bits not messages
- computation much cheaper than communication
Routing based on both correlation and geography
Slepian-Wolf coding

5. Iterative Decoding Techniques
LDPC decoding based on the iterative sum-product algorithm
Distributed computation based on message passing
This algorithm be applied to distributed estimation tasks as well
What if messages are corrupted?
Goals:
- develop fault tolerant message passing schemes
- develop feedback codes p1 p2 b1 b2 b3 b4