Statistical decision making method for cognitive radio

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

doi:10.1109/ictel.2012.6221311

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Since the environment is described statistically the errors of the radio sensors are taken into account in the decision making and this reduces the probabilities of false alarm and false decision with a reduced computational complexity. We have justified this result in another work [13] where we applied the cognitive engine designed in this paper to the scenario of adapting the use of the equalizer in the receiver chain according to the level of the inter-symbol interferences. In our future work, we will use our cognitive engine to treat other scenarios of adapting the radio receiver.…”

Section: Discussionmentioning

confidence: 71%

Cognitive engine design for cognitive radio

Bourbia

Achouri

Grati

et al. 2012

2012 International Conference on Multimedia Computing and Systems

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The work presented in this paper consists of designing a cognitive engine for a cognitive radio receiver. This engine must provide to the radio receiver the ability to be aware of its environment and to make decisions about actions of reconfiguration; these actions aim to adapt the receiver architecture to the state of the environment. In our design we develop a decision making method based on a statistical modeling of the environment. To show the decision performance of the method, we treat one example of a scenario of reconfiguration by applying the cognitive engine; it is to decide if there is a problem of a weak signal or not.

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Section: Discussionmentioning

confidence: 71%

Cognitive engine design for cognitive radio

Bourbia

Achouri

Grati

et al. 2012

2012 International Conference on Multimedia Computing and Systems

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“…For a multipath channel from length L , ( h 0 ,..., h L − 1 ), disturbed by an added Gaussian noise, we have defined, in previous works [ Bourbia et al , , ], two metrics required for the evaluation of the environment and for the decision making. These metrics are SNR p and ISI which represent respectively the signal‐to‐noise ratio, measured for the path that has the highest energy, and the power of the intersymbol interference.…”

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

confidence: 99%

“…The estimation of the two metrics is based on the channel estimation. In Bourbia et al [] we have made a study on different algorithms of channel estimation proposed in the literature. After this study we concluded that the LMS (Least Square Error) algorithm is the most appropriate in our case for the estimation of SNR p and ISI since it provides the best compromise between the minimum estimation error and the minimum computational complexity.…”

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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“…In this section, we will describe the method that we have developed in order to determine the decision rule to make decision for using or not using the equalizer from the statistical modeling of the environment [15].…”

Section: Statistical Modeling Of the Environment And Decisionmentioning

confidence: 99%

Equalizer's Use Limitation for Complexity Reduction in a Green Radio Receiver

Bourbia

Guennec

Palicot

et al. 2013

Journal of Computer Networks and Communications

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is work is about reducing energy consumption in the receiver chain by limiting the use of the equalizer. It is to make the radio receiver aware of its environment and able to take decision to turn on or off the equalizer according to its necessity or not. When the equalizer is off, the computational complexity is reduced and the rate of reduction depends on the percentage of time during which this component is disabled. In order to achieve this scenario of adapting the use of the equalizer, we need to develop a decisionmaking technique that provides the receiver with the capacities of awareness and adaptability to the state of its environment. For this, we improve a technique based on a statistical modeling of the environment by de�ning two metrics as channel quality indicators to evaluate the effect of the intersymbol interferences and the channel fading. e statistical modeling technique allows to take into account the impact of the uncertainties of the estimated metrics on the decision making.

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Statistical decision making method for cognitive radio

Cited by 4 publications

References 5 publications

Cognitive engine design for cognitive radio

Cognitive engine design for cognitive radio

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

Equalizer's Use Limitation for Complexity Reduction in a Green Radio Receiver

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