Cognitive engine design for cognitive radio

Bourbia, Salma; Achouri, Madiha; Grati, Khaled; Guennec, Daniel Le; Ghazel, Adel

doi:10.1109/icmcs.2012.6320323

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Our method of decision making is a statistical approach based on the statistical modeling of the radio environment [ Bourbia et al , , ]. The first step is to characterize statistically the observations of the metrics SNR p and ISI by determining their statistical parameters.…”

Section: Decision‐making Methods By Statistical Modeling Of the Radio mentioning

confidence: 99%

Decision‐making scenarios for reducing the computational complexity in a green cognitive radio receiver

et al. 2014

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(2014), Decision-making scenarios for reducing the computational complexity in a green cognitive radio receiver Radio Science, 49, 861-889, doi:10.1002 Abstract The idea that we highlight in this paper is how to reduce the computational complexity by limiting the processing in the receiver chain. For this we seek for limiting the use of the beamforming process and the equalization process according to the conditions. Indeed, with the cognitive features the receiver observes its environment and decides to either keep or turn off these processes without degrading its performances. The decision method that we developed is based on the statistical modeling of the radio environment, and the purpose of this approach is to minimize the percentage of bad decisions by considering the errors of observation. Furthermore, we first address the two decision scenarios separately and we seek to prove that the decisions to turn off the equalizer and the beamforming, when they are not necessary, lead to reduce the computational complexity of the receiver. Then we focus on the behavior of the receiver facing both decision scenarios. Thus, we formulate the decision problem in two different ways: in the first case we consider that the receiver performs joint decisions about the two operations of beamforming and equalization, in the second case we suppose that it handles the two decision scenarios sequentially. We compare then the performance of the receiver in the two cases.

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Section: Decision‐making Methods By Statistical Modeling Of the Radio mentioning

confidence: 99%

Decision‐making scenarios for reducing the computational complexity in a green cognitive radio receiver

et al. 2014

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“…By fixing a maximum probability of false alarm tolerated α = 1 % , we determine new thresholds of evaluation, K SNR and K ISI that consider the estimation errors of the sensors by introducing their statistical parameters. In , we explain the resolution of the hypothesis test with the Neyman Pearson technique for only the SNR p metric. By following the same approach for the ISI metric, we obtain in Equations and the result of this resolution that represents the decision rule δ to turn off the equaliser:

δ MathClass-punc: {\overset{μ}{true}}_{ISI}^{ML} ⩽ K_{ISI} or {\overset{μ}{true}}_{SN R_{p}}^{ML} ⩽ K_{SNR}

With

\begin{matrix} aligned \\ right K_{SNR} & left = λ_{SNR} + \frac{{\overset{MathClass-op̂}{σ}}_{SN R_{p}}^{ML}}{\sqrt{n}} \times F^{- 1} MathClass-open(α MathClass-close) & right \\ right K_{ISI} & left = λ_{ISI} + \frac{{\overset{MathClass-op̂}{σ}}_{ISI}^{ML}}{\sqrt{n}} \times F^{- 1} MathClass-open(α MathClass-close) \\ right \end{matrix}

F − 1 is the inverse function of F , and

F (x) MathClass-rel= {falsefalse}_{MathClass-op\int}^{MathClass-bin- MathClass-rel\infty} \frac{1}{2 π} e^{MathClass-bin- \frac{t^{2}}{2}} dt

is the standard normal distribution function.…”

Section: Green Cognitive Radio Scenariomentioning

confidence: 99%

Management architecture for green cognitive radio equipments

Lazrak

Bourbia

Moy

et al. 2013

Trans. Emerging Tel. Tech.

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We assert in this paper that a management architecture has to be added to usual signal processing chain of radio equipment in order to integrate green management capabilities. The proposed architecture is based on our previous work on hierarchical and distributed cognitive radio (CR) architecture management for CR equipments. We assert that, at the level of an equipment, green radio can be considered as a subset of CR. A model‐based approach is derived for the design of green radio equipments. As an example, we address in this paper a green scenario, which consists in bypassing the equaliser in function of signal to noise ratio and inter‐symbol interference levels at a receiver. Then we show how we save energy, thanks to a complexity reduction. Both signal processing and implementations views are given for this scenario, which shows how our approach helps converting principles into reality. Copyright © 2013 John Wiley & Sons, Ltd.

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“…In [18], for example, a load balancing algorithm based on a CE is proposed, but no discussion is provided on how the cross-layer information required to implement it should be conveyed and exchanged. In [19] a CE that relies on statistical modeling of environment parameters and hypothesis testing is proposed: in this case a list of parameters that need to be taken into account is provided, but methods to collect, exchange and feed to the CE such information are not discussed. Conversely, our work focuses on the identification of the interactions and information flows between the CE and the protocol stack layers that are necessary to support this cognitive behavior.…”

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Cross-Layer Analysis in Cognitive Radio—Context Identification and Decision Making Aspects

Kliks

Triantafyllopoulou

Nardis

et al. 2015

IEEE Trans. Cogn. Commun. Netw.

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Abstract-Research on context-aware communications recently led to the introduction of features and algorithms relying on the presence and rich, accurate context information, requiring however the introduction of cross-layer information exchanges. Cognitive radio (CR), in particular, is expected to benefit from context awareness, as the cognitive engine (CE) relies on the availability of multiple information sources to operate efficiently. In this context, this work delivers a detailed, yet concise classification and description of the information exchanged in a CR network between the layers of a generic protocol stack, and between each layer and the CE. For each layer, the key services provided and delivered are presented, followed by a catalogue of exchanged parameters. The analysis, supported by a set of use cases providing a quantitative assessment of the impact of crosslayer information exchanges in a CR framework, is the basis for the for the discussion of key implementation challenges and the identification of the most promising partition of functions and tasks between layers and CE.

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Cognitive engine design for cognitive radio

Cited by 4 publications

References 7 publications

Decision‐making scenarios for reducing the computational complexity in a green cognitive radio receiver

Decision‐making scenarios for reducing the computational complexity in a green cognitive radio receiver

Management architecture for green cognitive radio equipments

Cross-Layer Analysis in Cognitive Radio—Context Identification and Decision Making Aspects

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