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Causal Sampling, Compressing, and Channel Coding of Streaming Data


Guo, Nian (2023) Causal Sampling, Compressing, and Channel Coding of Streaming Data. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/n201-ca08.


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). In the causal sampling scenario, a sampler observes a continuous-time source process and causally decides when to transmit real-valued samples of it under a constraint on the average number of samples per second; an estimator uses the causally received samples to approximate the source process in real time. We propose a causal sampling policy that achieves the best tradeoff between the sampling frequency and the end-to-end real-time estimation distortion for a class of continuous Markov processes. In the causal lossy compressing scenario, the sampling frequency constraint in the causal sampling scenario is replaced by a rate constraint on the average number of bits per second. We propose a causal code that achieves the best causal distortion-rate tradeoff for the same class of processes. In the causal JSCC scenario, the noiseless channel and the continuous-time process in the previous scenarios are replaced by a discrete memoryless channel with feedback and a sequence of streaming symbols, respectively. We propose a causal joint sourcechannel code that achieves the maximum exponentially decaying rate of the error probability compatible with a given rate. Remarkably, the fundamental limits in the causal lossy compressing and the causal JSCC scenarios achieved by our causal codes are no worse than those achieved by the best non-causal codes. In addition to deriving the fundamental limits and presenting the causal codes that achieve the limits, we also show that our codes apply to control systems, are resilient to system deficiencies such as channel delay and noise, and have low complexities.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Causal code, sampling, source coding, joint source-channel coding, remote estimation, streaming, reliability function, rate-distortion theory, control
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Electrical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Kostina, Victoria
Thesis Committee:
  • Effros, Michelle (chair)
  • Bruck, Jehoshua
  • Hassibi, Babak
  • Kostina, Victoria
Defense Date:5 August 2022
Funding AgencyGrant Number
Record Number:CaltechTHESIS:08182022-183119533
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Chapter III adapted for Chapter III adapted for Chapters II-III adapted for Chapters II-III adapted for Chapter IV adapted for Chapter IV adapted for Chapter IV
Guo, Nian0000-0003-4490-328X
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15004
Deposited By: Nian Guo
Deposited On:26 Aug 2022 15:27
Last Modified:07 Jun 2023 17:07

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