A Continuity Theory of Source Coding over Networks
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A Continuity Theory of Source Coding over Networks WeiHsin Gu, Michelle Effros, Mayank Bakshi, and Tracey Ho FLoWS PI Meeting, Washington DC, September 2008 PowerPoint PPT Presentation


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A Continuity Theory of Source Coding over Networks WeiHsin Gu, Michelle Effros, Mayank Bakshi, and Tracey Ho FLoWS PI Meeting, Washington DC, September 2008. Status Quo. Achievable rate regions are hard to characterize. Only solved for some small networks and multicast networks

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A Continuity Theory of Source Coding over Networks WeiHsin Gu, Michelle Effros, Mayank Bakshi, and Tracey Ho FLoWS PI Meeting, Washington DC, September 2008

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A continuity theory of source coding over networks weihsin gu michelle effros mayank bakshi and tracey ho flows

A Continuity Theory of Source Coding over Networks WeiHsin Gu, Michelle Effros, Mayank Bakshi, and Tracey Ho FLoWS PI Meeting, Washington DC, September 2008


Status quo

Status Quo

Achievable rate regions are hard to characterize.

  • Only solved for some small networks and multicast networks

  • Inner (achievable) and upper (converse) bounds are derived for some example networks

    Open questions:

  • Are those earlier derived bounds tight?

  • Does single-letter characterization always exist?


New insights

New Insights

  • Develop and investigate some abstract properties of the achievable rate regions

  • As functions of probability distribution and distortion vector, are the achievable rate regions continuous?

    Motivations

  • Understand the existence of 1-letter characterizations

  • Applications: estimation of achievable rate regions

    Why is it hard?

  • Block length is unbounded in the definition of the achievable rate regions

  • Don’t know much about the achievable rate regions


Formulation and notation

Formulation and Notation

  • Define a family of network source coding problems which contains the prior example networks as special cases. The family contains four subfamilies

    • Lossless non-functional

    • Lossy non-functional

    • Lossless functional

    • Lossy functional

  • : Source : Side information

  • : Probability distribution : Distortion vector

  • : Lossy rate region

  • : Lossless rate region


Main results

Main Results

  • is concave in

    • is continuous in when

    • For any non-functional source coding problem, is continuous in for all

  • For super-source networks, both and are continuous in for all


A continuity theory of source coding over networks weihsin gu michelle effros mayank bakshi and tracey ho flows

Continuity w.r.t.

  • is continuous in if and only if

    • is upper semi-continuous in

    • is lower semi-continuous in

  • We show that the achievable rate regions are inner semi-continuous in for

    • Lossless non-functional

    • Lossy non-functional

    • Lossy functional


A possible solution

A Possible Solution

  • We conjecture that outer semi-continuity w.r.t. is true when random variables have finite alphabets

  • A possible approach: for any network , consider the super-source network

  • If the above equality is true, then is upper semi-continuous in


Next phase goals

Next-Phase Goals

  • Prove or disprove upper semi-continuity w.r.t. for general networks.

  • Characterize a larger family of networks where upper semi-continuity holds

  • Investigate some other useful abstract properties

  • Understand the existence of achievable rate regions


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