Stationary and cyclostationary random process models

Skinner, B.J.; Ingels, F.M.; Donohoe, J. Patrick

doi:10.1109/secon.1994.324356

Cited by 6 publications

(2 citation statements)

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“…In layman's terms, a cyclostationary stochastic process possesses nonobvious periodicity "hidden" in its statistical properties. A stochastic process x(t) is defined to be first order cyclostationary with period T if [78]…”

Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

“…WSCS is found in many common signal types including sine wave carrier modulated signals and those that use cyclic prefixes or repeated spreading. Real life signal message data (bitstream) is found to closely approximate a stationary random process [78] so modulation by a repeating carrier, for instance amplitude modulation (AM), forms a cyclostationary random process with period that of the carrier and SCF responses at twice the carrier frequency.…”

Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

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Analysis and Implementation of Reinforcement Learning on a GNU Radio Cognitive Radio Platform

Yu¹

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<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>

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Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

Section: Cyclostationary Feature Detectionmentioning

confidence: 99%

Analysis and Implementation of Reinforcement Learning on a GNU Radio Cognitive Radio Platform

Yu¹

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Cyclostationary model in DS spread spectrum communication system

Gong

2002

J. of Electron.(China)

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Circuits and techniques for high-resolution measurement of on-chip power supply noise

Alon

Stojanović

Horowitz

2005

IEEE J. Solid-State Circuits

132

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A technique for characterizing the cyclically time varying statistical properties and spectrum of power supply noise using only two on-chip samplers is presented. The samplers utilize a voltage-controlled oscillator to perform highresolution analog-to-digital conversion with minimal hardware. The measurement system is implemented in a 0.13pm process as part of a high-speed link transceiver.Measurement results showing the cyclostationary behavior of power supply noise are presented.

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Stationary and cyclostationary random process models

Cited by 6 publications

References 5 publications

Analysis and Implementation of Reinforcement Learning on a GNU Radio Cognitive Radio Platform

Analysis and Implementation of Reinforcement Learning on a GNU Radio Cognitive Radio Platform

Cyclostationary model in DS spread spectrum communication system

Circuits and techniques for high-resolution measurement of on-chip power supply noise

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