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PhD Thesis Defense

Friday, August 5, 2022
10:00am to 11:00am
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Online Event
Causal sampling, compressing, and channel coding of streaming data
Nian Guo, Graduate Student, Electrical Engineering, California Institute of Technology,

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With the emergence of the Internet of Things, communication systems, such as those employed in distributed control and tracking scenarios, are becoming increasingly dynamic, interactive, and delay-sensitive. The data in such real-time systems arrive at the encoder progressively in a streaming fashion. An intriguing question is: what codes can transmit streaming data with both high reliability and low latency? Classical non-causal (block) encoding schemes can transmit data reliably but under the assumption that the encoder knows the entire data block before the transmission. While this is a realistic assumption in delay-tolerant systems, it is ill-suited to real-time systems due to the delay introduced by collecting data into a block. This thesis studies causal encoding: the encoder transmits information based on the causally received data while the data is still streaming in and immediately incorporates the newly received data into a continuing transmission on the fly. This thesis investigates causal encoding of streaming data in three scenarios: causal sampling, causal lossy compressing, and causal joint source-channel coding (JSCC). For each scenario, we derive the fundamental limit and present the causal code that achieves the limit. We show that our causal codes apply to control systems, are resilient to system deficiencies such as channel delay and noise, and have low complexities.

For more information, please contact Tanya Owen by email at [email protected].