Publication detail

Enhanced Spectrum Planning in Cognitive System Based on Reinforcement Learning

URBAN, R. HUTOVÁ, E. NEŠPOR, D.

Original Title

Enhanced Spectrum Planning in Cognitive System Based on Reinforcement Learning

English Title

Enhanced Spectrum Planning in Cognitive System Based on Reinforcement Learning

Type

conference paper

Language

en

Original Abstract

This paper presents preliminary results of interference less spectrum planning which is performed by reinforcement learning. The frequency planning of the wireless services is very difficult in current overfilled spectrum situation since it is nearly impossible to find spectral hole worldwide to deploy a new wireless service. The possible solution is an open dynamic spectrum access which could be implemented as a part of cognitive radio. Moreover, the modern wireless standards such as LTE-A partly implement cognitive radio improvements, e.g. carrier aggregation system, which enables using unused parts of the frequency spectrum to decrease interference and increase data throughput. It could be realised both the intra-band and the inter-band solution. According to the measured data of the spectrum situation in various environments, we prepared best case of channel switching in LTE-A and WI-FI systems, which is based on the reinforcement learning to minimize interference with primary users represented by measured data. Using this technique, we are capable to obtain very low misdetection probability and large variety in channel switching.

English abstract

This paper presents preliminary results of interference less spectrum planning which is performed by reinforcement learning. The frequency planning of the wireless services is very difficult in current overfilled spectrum situation since it is nearly impossible to find spectral hole worldwide to deploy a new wireless service. The possible solution is an open dynamic spectrum access which could be implemented as a part of cognitive radio. Moreover, the modern wireless standards such as LTE-A partly implement cognitive radio improvements, e.g. carrier aggregation system, which enables using unused parts of the frequency spectrum to decrease interference and increase data throughput. It could be realised both the intra-band and the inter-band solution. According to the measured data of the spectrum situation in various environments, we prepared best case of channel switching in LTE-A and WI-FI systems, which is based on the reinforcement learning to minimize interference with primary users represented by measured data. Using this technique, we are capable to obtain very low misdetection probability and large variety in channel switching.

Keywords

cognitive radio, spectrum sharing, spectrum survey

RIV year

2013

Released

15.08.2013

ISBN

978-1-934142-26-4

Book

Proceedings of PIERS 2013 in Stockholm

Pages from

759

Pages to

762

Pages count

4

Documents

BibTex


@inproceedings{BUT101778,
  author="Robert {Urban} and Eliška {Vlachová Hutová} and Dušan {Nešpor}",
  title="Enhanced Spectrum Planning in Cognitive System Based on Reinforcement Learning",
  annote="This paper presents preliminary results of interference less spectrum planning which is performed by reinforcement learning. The frequency planning of the wireless services is very difficult in current overfilled spectrum situation since it is nearly impossible to find spectral hole worldwide to deploy a new wireless service. The possible solution is an open dynamic spectrum access which could be implemented as a part of cognitive radio. Moreover, the modern wireless standards such as LTE-A partly implement cognitive radio improvements, e.g. carrier aggregation system, which enables using unused parts of the frequency spectrum to decrease interference and increase data throughput. It could be realised both the intra-band and the inter-band solution. According to the measured data of the spectrum situation in various environments, we prepared best case of channel switching in LTE-A and WI-FI systems, which is based on the reinforcement learning to minimize interference with primary users represented by measured data. Using this technique, we are capable to obtain very low misdetection probability and large variety in channel switching.",
  booktitle="Proceedings of PIERS 2013 in Stockholm",
  chapter="101778",
  howpublished="online",
  year="2013",
  month="august",
  pages="759--762",
  type="conference paper"
}