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Software Defined Radios pp 109–128Cite as

Distributed Optimization of Local Area Networks

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Part of the book series: Signals and Communication Technology ((SCT))

Abstract

An important task of a cognitive radio is to learn and calibrate its behavior in the environment. How to achieve this efficiently is illustrated in this chapter for IEEE 802.11 networks that aim at minimizing the co-channel interference in a distributed way.

A novel control algorithm, Spatial Learning, is proposed, which learns the optimal operating point in a 3D design space at run-time. The learner interprets how the environment reacts to the selected actions and adapts his actions accordingly. Simulation-based experiments illustrate the trade-offs which need to be made, and the gains that can eventually be achieved.

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Notes

  1. 1.

    We use Jain’s Fairness Index as an indicator for fairness. This index is calculated as follows: \(f =\frac{(\sum_{i=1}^{n}S_{i})^{2}}{n\sum_{i=1}^{n}S_{i}^{2}}\) [40].

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Author information

Authors and Affiliations

  1. SSET/wireless, IMEC, Kapeldreef 75, Leuven, 3001, Belgium

    Sofie Pollin

  2. Bell Labs, Alcatel-Lucent, Copernicuslaan 50, Antwerpen, 2018, Belgium

    Michael Timmers

  3. IMEC VZW, Kapeldreef 75, Leuven, 3001, Belgium

    Dr. Liesbet Van der Perre

Authors
  1. Sofie Pollin
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  2. Michael Timmers
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  3. Dr. Liesbet Van der Perre
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Correspondence to Sofie Pollin .

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Pollin, S., Timmers, M., Van der Perre, L. (2011). Distributed Optimization of Local Area Networks. In: Software Defined Radios. Signals and Communication Technology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1278-2_7

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  • DOI: https://doi.org/10.1007/978-94-007-1278-2_7

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-1277-5

  • Online ISBN: 978-94-007-1278-2

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