Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios

Chaudhari, Sachin; Koivunen, Visa; Poor, H. Vincent

doi:10.1109/tsp.2009.2019176

Cited by 261 publications

(207 citation statements)

References 21 publications

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“…Instead, second order cyclostationarity can be employed to detect the periodicity of the primary signal statistics at the cost of increased complexity, long latency, and high sensitivity to sampling error [10]. Furthermore, the autocorrelation detection scheme exploits the non-zero average autocorrelation at a time displacement in the signal to provide flexible and reliable spectrum sensing [11], [12]. Table 1 provides a brief summary of the most popular standalone spectrum sensing techniques in terms of their most applicable application scenarios.…”

Section: Non-blind Sensingmentioning

confidence: 99%

Comparison of reliability, delay and complexity for standalone cognitive radio spectrum sensing schemes

2013

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The ability to reliably and autonomously identify unused frequency bands plays an extremely important role in cognitive radio networks . Relying on the spectrum sensing, ongoing licensed operation must not be compromised and the secondary spectrum usage efficiency should be maintained. Thus, it is critical to ensure that the confidence level of the estimated signal status satisfies the primary user's requirement, whilst keeping the delay and computational complexity to a minimum. This paper provides a comprehensive comparison in terms of performance, reliability and complexity of stand alone sensing schemes for various cognitive radio application areas. We first give some new results on reliability performance, and then evaluate the sensing time required to achieve the target performance. Finally, we compare the computational complexity of various sensing approaches by calculating the number of arithmetic operations required by each approach.

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Section: Non-blind Sensingmentioning

confidence: 99%

Comparison of reliability, delay and complexity for standalone cognitive radio spectrum sensing schemes

2013

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“…In this section we will describe the detector of [17], which also exploits the correlation of an OFDM signal, using knowledge of T d . The method of [17] was called an autocorrelationbased detector and it uses the empirical mean of the sample value products r[k], normalized by the received power, as test statistic.…”

Section: Autocorrelation-based Detectionmentioning

confidence: 99%

“…The method of [17] was called an autocorrelationbased detector and it uses the empirical mean of the sample value products r[k], normalized by the received power, as test statistic. More precisely, the test proposed in [17] is…”

Section: Autocorrelation-based Detectionmentioning

confidence: 99%

“…This shows that knowledge of the noise floor level is in practise not necessary in this case. If the noise power is completely unknown, only the second-order statistics detector (v) of [11] and the autocorrelation detector (vi) of [17] work. A general observation is that the second-order method (v) slightly outperforms the autocorrelation-based detector (vi) in all scenarios, in the region P d 0.15.…”

Section: Scenario 6: Multipath Rayleigh Fading (Figure 6)mentioning

confidence: 99%

“…A Reed-Solomon (RS) [204,188,17] shortened code is used as an outer channel block code. This code is obtained by shortening of the original systematic RS [255, 239, 17] code with generator polynomial given in [13, Section 4.3.2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spectrum sensing methods for detection of DVB-T signals in AWGN and fading channels

Danev

Axell

Larsson

2010

21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

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Abstract-In this paper, we consider spectrum sensing of DVB-T signals in different fading environments. We compare state-ofthe-art detectors including detectors based on pilot subcarriers, as well as detectors for general OFDM signals that exploit the correlation structure incurred by the cyclic prefix. Energy detection is also included for comparison. We show numerically that the choice of detector depends on the scenario, the detector requirements, and on the available prior knowledge. We also show that it is possible to obtain good detection performance by exploiting the correlation, even in a frequency selective channel.

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Cognitive Radio

2015

Wireless Communications

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Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios

Cited by 261 publications

References 21 publications

Comparison of reliability, delay and complexity for standalone cognitive radio spectrum sensing schemes

Comparison of reliability, delay and complexity for standalone cognitive radio spectrum sensing schemes

Spectrum sensing methods for detection of DVB-T signals in AWGN and fading channels

Cognitive Radio

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