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Interacting Multiple Model Based Detector to Compensate Power Amplifier Distortions in Cognitive Radio

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PROJECT TITLE :

Interacting Multiple Model Based Detector to Compensate Power Amplifier Distortions in Cognitive Radio

ABSTRACT:

For a battery driven terminal, the power amplifier (PA) efficiency should be optimized. Consequently, non-linearities might seem at the PA output in the transmission chain. To compensate these distortions, one answer consists of using a digital detector based on a Volterra model of each the PA and therefore the channel and a Kalman filter (KF) based algorithm to jointly estimate the Volterra kernels and the transmitted symbols. Here, we suggest addressing this issue when coping with cognitive radio (CR). During this case, further constraints should be taken under consideration. Since the CR terminal could switch from one sub-band to a different, the PA non-linearities may vary over time. So, we have a tendency to propose to style a digital detector primarily based on an interacting multiple model combining numerous KF based mostly estimators using totally different model parameter dynamics. This makes it doable to track the time variations of the Volterra kernels while keeping correct estimates when those parameters are static. Furthermore, the single and multicarrier cases are addressed and validated by simulation results. Our resolution corresponds to a compromise between computational price and bit-error-rate performance.


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Interacting Multiple Model Based Detector to Compensate Power Amplifier Distortions in Cognitive Radio - 4.9 out of 5 based on 78 votes

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