Link-Layer Abstractions for Utility-Based Optimization in Cognitive Wireless Networks

Janne  Riihijarvi; Matthias  Wellens; Petri  Mahonen

1st International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

Research Article

Link-Layer Abstractions for Utility-Based Optimization in Cognitive Wireless Networks

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1109/CROWNCOM.2006.363448,
    author={Janne  Riihijarvi and Matthias  Wellens and Petri  Mahonen},
    title={Link-Layer Abstractions for Utility-Based Optimization in Cognitive Wireless Networks},
    proceedings={1st International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications},
    publisher={IEEE},
    proceedings_a={CROWNCOM},
    year={2007},
    month={5},
    keywords={Application software Chromium Delay Engines Machine learning Measurement Proposals Protocols Quality of service Wireless networks},
    doi={10.1109/CROWNCOM.2006.363448}
}

Janne Riihijarvi
Matthias Wellens
Petri Mahonen
Year: 2007
Link-Layer Abstractions for Utility-Based Optimization in Cognitive Wireless Networks
CROWNCOM
IEEE
DOI: 10.1109/CROWNCOM.2006.363448

Janne Riihijarvi¹^,*, Matthias Wellens¹^,*, Petri Mahonen¹^,*

1: Department of Wireless Networks, RWTH Aachen University, Kackertstrasse 9, D-52072 Aachen, Germany.

*Contact email: jar@mobnets.rwth-aachen.de, mwe@mobnets.rwth-aachen.de, pma@mobnets.rwth-aachen.de

Abstract

We argue that utility functions are very natural tools for formulating optimization problems in cognitive wireless networks. However, their use mandates that the platform used supports well-defined APIs and abstractions for retrieving information necessary to calculate the application utilities. We present the design of a unified link-layer API that offers precisely this functionality in the context of link-layer information. The API developed offers powerful query and notification mechanisms, which considerably simplify the implementation of technology-independent reasoning engines for cognitive networks. Prototype implementations of the API show that despite the rich functionality offered, the implementation can be made very lightweight and fast, enabling optimization decisions even on per-frame basis with standard user terminals

Keywords: Application software Chromium Delay Engines Machine learning Measurement Proposals Protocols Quality of service Wireless networks

Published: 2007-05-21
Publisher: IEEE

: http://dx.doi.org/10.1109/CROWNCOM.2006.363448