Practical interactive scheme for extremum computation in distributed networks

Torabi, Solmaz; Ren, Jie; Walsh, John MacLaren

doi:10.1109/isit.2016.7541467

Cited by 2 publications

(2 citation statements)

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“…This curve was found via exhaustive search over all scalar quantization schemes. In addition to presenting this problem in detail and describing these curves, [41] also considers reduced complexity restricted to smaller quantizer search spaces. Finally, the curve "Homo.…”

Section: B Interactive Collocated Network Simulation Of the Omniscien...mentioning

confidence: 99%

“…While this does count as a form of interaction, requiring the users to send their messages in parallel substantially increases the rate required. Rate required to select a user attaining the max with 3 users each observing an independent RV with support {1, 2, 3, 4} when users take turns sending messages one at a time [43], [41]. The fundamental limit on the rate given by ( 22) is compared to that obtained by the best scalar quantizer to use in the collocated network interactive scheme (Optimal Scalar.…”

Section: B Interactive Collocated Network Simulation Of the Omniscien...mentioning

confidence: 99%

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A Framework for Rate Efficient Control of Distributed Discrete Systems

Ren,

Torabi,

Walsh

2017

Preprint

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A key issue in the control of distributed discrete systems modeled as Markov decisions processes, is that often the state of the system is not directly observable at any single location in the system. The participants in the control scheme must share information with one another regarding the state of the system in order to collectively make informed control decisions, but this information sharing can be costly. Harnessing recent results from information theory regarding distributed function computation, in this paper we derive, for several information sharing model structures, the minimum amount of control information that must be exchanged to enable local participants to derive the same control decisions as an imaginary omniscient controller having full knowledge of the global state. Incorporating consideration for this amount of information that must be exchanged into the reward enables one to trade the competing objectives of minimizing this control information exchange and maximizing the performance of the controller. An alternating optimization framework is then provided to help find the efficient controllers and messaging schemes. A series of running examples from wireless resource allocation illustrate the ideas and design tradeoffs.

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Section: B Interactive Collocated Network Simulation Of the Omniscien...mentioning

confidence: 99%

Section: B Interactive Collocated Network Simulation Of the Omniscien...mentioning

confidence: 99%