Autoregressive Spectrum Hole Prediction Model for Cognitive Radio Systems

Wen, Zhigang; Luo, Tao; Xiang, Weidong; Majhi, Sudhan; Ma, Yunhong

doi:10.1109/iccw.2008.34

Cited by 66 publications

(39 citation statements)

References 7 publications

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“…The primary transmitter of interest in the cognitive radio is identical to the non-cooperative transmitters in [26]- [34]. In other words, the primary transmitter in this paper is in the identical situation as the transmitters in [26]- [34], which may perform power control as in the communication system and experience shadowing effects. The detector at the secondary user in the cognitive radio is in the identical situation as the detectors in [26]- [34], which operate without cooperation of the signal emitters.…”

mentioning

confidence: 99%

“…The detector at the secondary user in the cognitive radio is in the identical situation as the detectors in [26]- [34], which operate without cooperation of the signal emitters. With the experimental results and commonly practiced adoption in [26]- [34], we adopt the AR model to approximate the signals emitted from the primary transmitters in the cognitive radio. The received signals, consisting of the AR processes and noises, can be represented by the autoregressive moving average (ARMA) processes [36].…”

mentioning

confidence: 99%

“…In other words, the primary transmitter in this paper is in the identical situation as the transmitters in [26]- [34], which may perform power control as in the communication system and experience shadowing effects. The detector at the secondary user in the cognitive radio is in the identical situation as the detectors in [26]- [34], which operate without cooperation of the signal emitters. With the experimental results and commonly practiced adoption in [26]- [34], we adopt the AR model to approximate the signals emitted from the primary transmitters in the cognitive radio.…”

mentioning

confidence: 99%

“…In [32], the AR is used to model the signals emitted by the non-cooperative target, where the AR modeling is verified through experimental data. Besides, the AR process is used to model the unknown digitally modulated signals emitted by non-cooperative targets [33], spectrum holes in the cognitive radio [34], and the unknown color noises [35]. The primary transmitter of interest in the cognitive radio is identical to the non-cooperative transmitters in [26]- [34].…”

mentioning

confidence: 99%

“…Besides, the AR process is used to model the unknown digitally modulated signals emitted by non-cooperative targets [33], spectrum holes in the cognitive radio [34], and the unknown color noises [35]. The primary transmitter of interest in the cognitive radio is identical to the non-cooperative transmitters in [26]- [34]. In other words, the primary transmitter in this paper is in the identical situation as the transmitters in [26]- [34], which may perform power control as in the communication system and experience shadowing effects.…”

mentioning

confidence: 99%

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Sequential Likelihood Ratio Test under Incomplete Signal Model for Spectrum Sensing

Chung

2013

IEEE Trans. Wireless Commun.

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Abstract-Detecting the existence of the transmitter emitting signals is an important mechanism in many applications, e.g., the spectrum sensing in the cognitive radio. In conventional detection schemes, the predefined number of samples is taken for detection and the statistics of the signals are assumed to be available in the signal model. However, under the ubiquitous fading effects and the non-cooperation of the targets, the signal statistics are not accurately obtainable at the detector. In this paper, we propose a sequential detector operating on the signal model described by the autoregressive moving average (ARMA) process without assuming known coefficients. The sequential detector for the ARMA model is derived by using the likelihood ratio test framework and the predictive distributions of the ARMA process. The novelties the proposed sequential detector include: 1) performing detection without requiring complete knowledge of the signal; 2) using smaller number of samples to reach the decision on average; and 3) allowing user-specified probabilities of detection and false alarm. We derive the approximate average number of samples required to reach the decision. The energy detector and sequential energy detector are compared with the proposed sequential detector by simulations. The results show the sequential detector uses the smaller average number of samples than the energy detector and sequential energy detector to termination.

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