Second-Order Cyclostationarity of Mobile WiMAX and LTE OFDM Signals and Application to Spectrum Awareness in Cognitive Radio Systems

Al-Habashna, Ala’a; Dobre, Octavia A.; Venkatesan, R.; Popescu, Dimitrie C.

doi:10.1109/jstsp.2011.2174773

Cited by 109 publications

(42 citation statements)

References 20 publications

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“…(1) the spectral line regeneration (Section 6.1, [114]) [79,335,357]; (2) the statistical test for presence of cyclostationarity (Section 6.3, [63]) [6,149,178,233,271,270,303,302]; (3) the statistical test for presence of spectral coherence (Section 6.4, [156]) or the cycle frequency domain profile (Section 7.2, [187]) [219,355]; (4) higher-order cyclostationarity properties (Section 6.7) [82,221,279,327,329].…”

Section: Spectrum Sensing and Signal Classificationmentioning

confidence: 99%

“…In addition, cyclic statistics have been derived for: longcode DSSS signals [107], continuous phase modulated (CPM) signals [45,258], digital pulse streams modulated by cyclostationary sequences [268], multicarrier modulated signals [377], UWB signals [266,267,354], OFDM signals [59], OFDM and single carrier linearly digitally modulated signals [279], WiMAX and long-term evolution (LTE) OFDM signals [6], spatial multiplexing OFDM (SM-OFDM) and Alamouti coded OFDM (AL-OFDM) signals [181], block transmitted SCLD (BT-SCLD) signals [379], LTE SC-FDMA signals [178], LTE advanced signals [336], BOC signals [228], and global position system (GPS) signals [256].…”

Section: Cyclic Spectral Analysis Of Man-made Signalsmentioning

confidence: 99%

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Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

184

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Section: Spectrum Sensing and Signal Classificationmentioning

confidence: 99%

Section: Cyclic Spectral Analysis Of Man-made Signalsmentioning

confidence: 99%

Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

184

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“…To detect the existence/non-existence of OFDM signals we can use functions of the autocorrelation lags, where the autocorrelation is based on (6). Therefore, the autocorrelationbased decision statistic is given by [5] Υ AD (y) = L l=1 w l Re {r l } r 0…”

Section: Autocorrelation Based-spectrum Sensingmentioning

confidence: 99%

“…ED requires an accurate knowledge of the noise statistics and its performance is highly sensitive to any inaccuracy in this knowledge. On the other hand, feature detection schemes such as autocorrelation detectors (AD) [4] and cyclostationarity detectors (CO) [6], [7] require information about the statistical properties of the detected signals. AD and CO algorithms achieve higher probability of correct detection (PD) than ED in practical communication systems [3].…”

Section: Introductionmentioning

confidence: 99%

Computer vision aided OFDM-based standards detection and classification technique for cognitive radio systems

Guibène

Khirallah

Slock

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper presents an innovative spectrum sensing scheme for Orthogonal Frequency Division Multiplexing (OFDM) signals based on enhancing the performance of the popular autocorrelation detectors (AD) using non-linear image processing methods. These methods improve the detection accuracy of the AD under particular false-alarm constraints. The proposed scheme is used in the detection of two OFDM systems, Long Term Evolution (LTE) and DVB terrestrial digital TV (DVB-T) under low signal to noise ratio (SNR) channel conditions. Results obtained show significant improvement in correct signals detection/classification up to 18% and 48% at a false-alarm of 5% and low SNR conditions equal to -18dB, using the combined AD and image processing scheme for the detection of LTE and DVB-T signals, respectively.

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“…Literatures have discussed the coexistence problem of the LTE with WiMax since their operating spectrum have overlap, e.g., LTE band 40 signal is in Time-Division Duplexing (TDD) mode operating from 2.3GHz to 2.4GHz, and 2.3GHz has been published as one licensed spectrum profile for WiMax. Authors in [7] use cyclostationarity to distinguish WiMax and LTE OFDM signals based on the structure difference between these two technologies. However, to our best knowledge, there is no reference considering the coexistence problem of LTE with WiFi technologies.…”

Section: Introductionmentioning

confidence: 99%

Energy ratio based WiFi OFDM leakage detection in LTE downlink for in-device interference avoidance

Plumb

2014

2014 IEEE 11th Consumer Communications and Networking Conference (CCNC)

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Abstract-To allow users to access wireless networks and services ubiquitously, more and more user equipments (UEs) are equipped with multiple radio transceivers. However, due to the proximity of transmit and receive frequencies of indevice wireless technologies, the transmitter (TX) of one in-device wireless technology can interfere with the reception of another wireless technology within the same device, and the spurious emissions of the aggressor TX may result in unacceptable interference at the victim receiver which is co-located in the same device. Without central control or cooperation of these transceivers, the receiver (RX) cannot determine the source of the interference. Hence, it has been attracting strong attention to detect in-device interference and adapt strategies to avoid harmful interference. In this paper, we consider the scenario of LTE coexisting with WiFi OFDM signal in the same device. Different from traditional spectrum sensing in cognitive radio, the WiFi leakage detection problem in this paper aims to detect WiFi leakage in the background of LTE signal and Gaussian noise (due to the short distance, AWGN channel will be dominant scenario). Therefore, traditional spectrum sensing algorithms such as energy detection cannot be applied directly. By identifying unique features of the WiFi leakage, we propose a simple yet effective detection algorithm based on energy ratio to successfully detect the WiFi leakage. Specifically, taking advantage of the unbalanced spectrum feature of the WiFi leakage, we utilize the energy ratio between the higher frequency component and the total frequency component as the detection metric. We theoretically analyze the energy ratio, and derive the detection probability and false alarm probability. Simulation results are presented to illustrate the effectiveness and efficiency of the proposed scheme, and validate the theoretical analysis.

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Second-Order Cyclostationarity of Mobile WiMAX and LTE OFDM Signals and Application to Spectrum Awareness in Cognitive Radio Systems

Cited by 109 publications

References 20 publications

Cyclostationarity: New trends and applications

Cyclostationarity: New trends and applications

Computer vision aided OFDM-based standards detection and classification technique for cognitive radio systems

Energy ratio based WiFi OFDM leakage detection in LTE downlink for in-device interference avoidance

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