CRN:
RADIO Frequency (RF) spectrum is an expensive and limited resource for wireless communications. The increasing demands for additional bandwidth have led to studies that indicate the spectrum assigned to primary license holders is under-utilized.Cognitive radio technology helps to use the RF spectrum more efficiently, by introducing secondary usage of the spectrum licensed to primary users (PU) but with a lower priority. A cognitive radio is able to change its transmitter parameters based on interaction with the environment. Secondary users (SU’s) equipped with cognitive radios can sense the spectrum and dynamically use spectrum holes in PU frequency bands for data transmission.
Fig: Cognitive Network
It is often assumed that the additive noise samples are statistically independent. Although the AWGN assumption is valid in several situation, the work in this paper is mainly motivated by situations that we have encountered where the noise exhibits significant correlation.. In such applications, the noise as experimentally measured presents several characteristics, one of them being correlation in the time domain. The noise models needed in these cases quickly become complicated and involve most often Markov transition models Any realistic cognitive radio environment would require to at least take into account at some level the noise correlation
ANALYSIS OF THE POWER AMPLIFIER NONLINEARITY ON THE POWER ALLOCATION IN COGNITIVE RADIO NETWORKS
RADIO Frequency (RF) spectrum is an expensive and limited resource for wireless communications. The increasing demands for additional bandwidth have led to studies that indicate the spectrum assigned to primary license holders is under-utilized.Cognitive radio technology helps to use the RF spectrum more efficiently, by introducing secondary usage of the spectrum licensed to primary users (PU) but with a lower priority. A cognitive radio is able to change its transmitter parameters based on interaction with the environment. Secondary users (SU’s) equipped with cognitive radios can sense the spectrum and dynamically use spectrum holes in PU frequency bands for data transmission.
Fig: Cognitive Network
It is often assumed that the additive noise samples are statistically independent. Although the AWGN assumption is valid in several situation, the work in this paper is mainly motivated by situations that we have encountered where the noise exhibits significant correlation.. In such applications, the noise as experimentally measured presents several characteristics, one of them being correlation in the time domain. The noise models needed in these cases quickly become complicated and involve most often Markov transition models Any realistic cognitive radio environment would require to at least take into account at some level the noise correlation
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ANALYSIS OF THE POWER AMPLIFIER NONLINEARITY ON THE POWER ALLOCATION IN COGNITIVE RADIO NETWORKS
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