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

Abstract

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.

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

“…To estimate the angle of arrival θ we performed in Bourbia et al [] a study on different techniques of estimation, and we concluded that the algorithm of CAPON is the best in our case for the compromise between the minimum estimation error and the minimum computational complexity. Now by following our decision‐making method of statistical modeling, we characterize statistically the observations of θ .…”

Management architecture for green cognitive radio equipments

“…We consider here a receiver with an equaliser that can be activated or disabled. The method of learning and decision making that we adopt is based on a statistical approach that we developed in our previous work . Our idea is to statistically model and characterise the radio environment using some techniques of the statistical inference like statistical estimation and statistical detection.…”

“…Based on these channel indicators and on their statistical characteristics, we can evaluate the current state of the channel and then decide if the equaliser is necessary in this case or not. The use of the statistical aspect of the channel quality indicators allows taking into account the estimation errors of these metrics in the decision making system, which affect the decision performance .…”

“…These radio metrics are based on the channel's coefficients. In our previous work , we have made a study to select the channel estimation technique to estimate SNR p and ISI . It was based on a trade‐off between a minimum estimation error and a minimum computational complexity.…”

“…We suppose that and are identically distributed and independent following the same distributions as two random variables, and . The analysis that we perform on the estimation of the radio metrics SNR p and ISI shows that a Gaussian distribution represents correctly the distributions of and . In order to estimate the statistical parameters of these observations, we use the technique of maximum likelihood estimator.…”