Comparison of energy detection using averaging and maximum values detection for dynamic spectrum access

Boksiner, Jeffrey; Dehnie, Sintayehu

doi:10.1109/sarnof.2011.5876479

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…In order to clarify the behavior of the proposed LSF preliminary method for AR/ARMA model prediction, which has been explored to remove the CT term, the prediction performance via the LSF algorithm was compared to the performance via the LDC algorithm. of PPM signal pulses can be obtained via many standard methods, such as energy detection algorithm [15]. The local 20 data series are treated as the training data to forecast the next one, and then go on with a slide prediction.…”

Section: Experiments and Analysismentioning

confidence: 99%

Parameter Estimation and Prediction of the Chirp and Stochastic Pulse Position Modulation Combined Signal

Zhang¹,

Xiong²,

Wang³

et al. 2013

JCM

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Recent work has proposed a certainty trend (CT) elimination technique employed for the auto-regressive/autoregressive and moving-average (AR/ARMA) model pulse position prediction. In this paper, we investigate the intra pulse parameter estimation and pulse position prediction of the chirp and stochastic pulse position modulation (CSPPM) combined signal. The quick dechirp method is adopted to the initial frequency and chirp rate estimation. To get a stationary data series satisfying the premise condition of the AR/ARMA model prediction, a least square fitting (LSF) scheme to remove the CT term contained in pulse position sequence is presented. Compared with the classic logarithmic difference conversion (LDC) smooth method, AR/ARMA prediction performance via LSF has a significant improvement, about 70% to 96% for AR prediction and 42% to 99% for ARMA prediction.  Index Terms-AR/ARMA model, prediction, stochastic pulse position modulation signal, least square fitting (LSF) I. INTRODUCTION Recently, the complex signal such as the chirp and stochastic pulse position modulation (CSPPM) combined signal is receiving more and more attention [1]-[3]. M. Kaveh and G.R. Cooper introduced the notion of the stochastic pulse position modulation (SPPM) signal in [4] and show that the removal of the velocity ambiguity requires the random delays instead of lowering the average repetition rate significantly. It is noted that CSPPM combined signal has a super low probability of intercept and anti-jamming performance by deriving its ambiguous function. Unfortunately, the surveillance of this non-cooperative signal is a challenge problem for radar reconnaissance [5], [6]. Recent years, researchers concentrated on the analysis of the SPPM signal's generation [7], distance measurement methods [8] and properties [9]. Few literatures reported the pulse position prediction for the further signal sorting and tracking. The purpose of this paper is to predict the combined signal's pulse occurrence time dependent on the live pulse position sequence, which can be treated as a discrete stationary random process in the presence of arbitrary time jitter. Inspired by that the stochastic time

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Section: Experiments and Analysismentioning

confidence: 99%

Parameter Estimation and Prediction of the Chirp and Stochastic Pulse Position Modulation Combined Signal

Zhang¹,

Xiong²,

Wang³

et al. 2013

JCM

View full text Add to dashboard Cite

show abstract

“…In , the optimal sensing time of the secondary user using energy detection is derived to maximize the average throughput and protect the primary user from harmful interference. In , performance of two different energy detection methods were compared, where the first (averaging) approach uses the signal power over the whole bandwidth, and the second (maximum) approach detects signal power over individual fast Fourier transform bins.…”

Section: Introductionmentioning

confidence: 99%

Sliding window energy detection for spectrum sensing under low SNR conditions

Tian

Blasch

et al. 2015

Wireless Communications

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For spectrum sensing, energy detection has the advantages of low complexity, rapid analysis, and requires no knowledge of the transmission signal, which makes it suitable for a wide range of applications. However, under low signal-to-noise ratio conditions, the required window length (or the time-bandwidth product) for energy detection to achieve a desired detection performance is large. In addition, conventional energy detection assumes that the detection tests are independent, that is, there is no overlap between individual detection tests. These properties significantly reduce the detection speed when energy detection is used for the continuous monitoring over a communication channel for the detection of signal transmission activities. In this paper, we propose a sliding window detection analysis with overlap among multiple tests. Algorithms for effective performance analysis of the proposed sliding window energy detection are proposed. The impact of window length on distribution of detection time is investigated. Simulation results on the proposed sliding window energy detection are also compared with the theoretically predicted and conventional energy detection performance estimates.

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“…It can reuse and share spectrum resource from the time, space and frequency dimension. In cognitive radio system [3], completely sensing for licensed spectrum will consume a large amount of time. The handoff time is required to be as short as possible to enhance the reliability and elusiveness in communication.…”

Section: Introductionmentioning

confidence: 99%

Work in progress: Spectrum data mining in the cognitive radio

Ren

Wang

Peng

et al. 2014

9th International Conference on Communications and Networking in China

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with the rapid development of wireless communications, the ever increasing demand for limited spectrum resources will eventually cause spectrum scarcity problem. Cognitive Radio offers a solution to this problem, which intelligently detect unused spectrum and share it, without harmful interference to primary users. It has been a research hotspot on how to analyze and utilize licensed spectrum to meet the increasing need for bands. Aiming at such a problem, this paper presents a CR system model and then proposes the scheme for spectrum data mining from the large-scale of TV spectrum sensing data. We designed three levels of information mining: statistical information, space clustering, and sequential pattern mining. The simulation results illustrate the effectiveness of the data mining scheme.

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Comparison of energy detection using averaging and maximum values detection for dynamic spectrum access

Cited by 10 publications

References 14 publications

Parameter Estimation and Prediction of the Chirp and Stochastic Pulse Position Modulation Combined Signal

Parameter Estimation and Prediction of the Chirp and Stochastic Pulse Position Modulation Combined Signal

Sliding window energy detection for spectrum sensing under low SNR conditions

Work in progress: Spectrum data mining in the cognitive radio

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