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Interaction of Communication, Computation, and Control in Wireless Networks

This presentation by Sekhar Tatikonda from Yale University explores the intertwined dynamics of communication, computation, and control within wireless networks. It addresses challenges such as the traditionally separate design of these fields and highlights key projects focusing on feedback in communication, control under constraints, and distributed detection in sensor networks. The goals include developing efficient feedback codes and fault-tolerant message-passing schemes while considering practical issues like noisy feedback and low power constraints in sensor networks.

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Interaction of Communication, Computation, and Control in Wireless Networks

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  1. The Interaction between Communication, Computation, and ControlSekhar 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 plant • 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 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

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