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.
This work is about developing a decision making method, based on statistical modeling, for a cognitive radio receiver. By characterizing statistically the radio metrics, the intelligent equipment will be capable of making decisions on actions of reconfigurations, in order to adapt to the state of the environment. Through the adaptability of the radio receiver to the environment, we try also to reduce energy consumption by treating one scenario of reconfigurability which consists of switching off the equalizer when it is not necessary. Since this component is complex, the operation of turning it off should reduce the computational complexity of the receiver chain.
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.
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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