Improved spectrum sensing for OFDM cognitive radio in the presence of timing offset

Shi, Zhenguo; McLernon, Des; Ghogho, Mounir; Wu, Zhilu

doi:10.1186/1687-1499-2014-224

Cited by 7 publications

(6 citation statements)

References 22 publications

(28 reference statements)

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“…The SS of OFDM signals transmitted according to the described transmission process has been analysed in different studies in the literature [ 10 , 11 , 19 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. In Table 1 , an overview of ED methods and their corresponding merits are presented.…”

Section: Related Work On the Ss Of Ofdm Signalsmentioning

confidence: 99%

“…Additionally, in Refs. [ 19 , 28 , 29 ], algorithms considering properties of the OFDM CP for SS were presented. The expression for selecting the sensing threshold is derived from Ref.…”

Section: Related Work On the Ss Of Ofdm Signalsmentioning

confidence: 99%

“…The ED algorithms proposed in Refs. [ 19 , 29 ] proved to be sensitive to the changes in timing offset. In Ref.…”

Section: Related Work On the Ss Of Ofdm Signalsmentioning

confidence: 99%

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A Survey on the Energy Detection of OFDM Signals with Dynamic Threshold Adaptation: Open Issues and Future Challenges

Lörincz

Ramljak²,

Begušić

2021

Sensors

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Cognitive radio (CR), as a concept based on the ability to detect and share the unutilised spectrum, has been envisioned as a promising candidate to improve the efficiency of frequency spectrum assignments. For the realisation of the CR concept, energy detection (ED), as one of the available spectrum sensing methods, is broadly considered because of its low computational complexity and implementation costs. Due to the vast usage of the orthogonal frequency division multiplexing (OFDM) technique in contemporary communication systems, the ED of OFDM signals in the CR networks has become important for practical realisation. Since the ED accuracy of the OFDM signals can be improved by the sensing threshold adaptation, this paper surveys the impact of noise variations and dynamic threshold (DT) adaptation on the ED performance of OFDM signals. Analyses were performed by the simulation of the ED related to OFDM signals transmitted in the margin or rate adaptive and combined margin and rate adaptive OFDM systems. The results obtained through extensive simulations provide fundamental insights into how different factors, including the transmission power, the signal to noise ratio, the false alarm probability and the sample quantity, affect the ED efficiency. Comprehensive analyses of the obtained results indicate the main ED weaknesses and how the appropriate selection of analysed factors can enhance the ED processes for different OFDM systems. The observed ED weaknesses were further thoroughly surveyed, and the open issues and challenges related to the enhancement of the main ED limitations have been elaborated. The presented survey results can serve as a basis for the improvement of a broadly accepted ED method in CR networks.

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Section: Related Work On the Ss Of Ofdm Signalsmentioning

confidence: 99%

“…Additionally, in Refs. [ 19 , 28 , 29 ], algorithms considering properties of the OFDM CP for SS were presented. The expression for selecting the sensing threshold is derived from Ref.…”

Section: Related Work On the Ss Of Ofdm Signalsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on the Energy Detection of OFDM Signals with Dynamic Threshold Adaptation: Open Issues and Future Challenges

Lörincz

Ramljak²,

Begušić

2021

Sensors

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“…OFDM based energy Detection algorithm is used in detecting the presence of the user in the spectrum with the timing offset. The accurate threshold is determined without calculating the timing offset [9]. Energy detection algorithm with AWGN is added to the measured signal for transmission.…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

Pethunachiyar

Sankaragomathi

2021

IJEECS

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<p class="IJASEITAbtract"><span>Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.</span></p>

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“…Some literatures have proposed a number of algorithms to solve these problems presented above. In [ 15 ], the authors provided an improved CP detector by constructing a likelihood ratio test (LRT) based on the multivariate p.d.f. 's of a particular autocorrelation vector.…”

Section: Introductionmentioning

confidence: 99%

Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks

Shi

Yin

et al. 2015

Sensors

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Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm “Differential Characteristics-Based OFDM (DC-OFDM)” for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a “Differential Characteristics-Based Cyclic Prefix (DC-CP)” detector and a “Differential Characteristics-Based Pilot Tones (DC-PT)” detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors.

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Improved spectrum sensing for OFDM cognitive radio in the presence of timing offset

Cited by 7 publications

References 22 publications

A Survey on the Energy Detection of OFDM Signals with Dynamic Threshold Adaptation: Open Issues and Future Challenges

A Survey on the Energy Detection of OFDM Signals with Dynamic Threshold Adaptation: Open Issues and Future Challenges

Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks

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