Spectrum Smoothing Method for OFDM Signals Detection in Frequency Selective Channel
Keywords:OFDM, smoothed spectrum, double thresholding, interval of orthogonality, frequency channel, frequency selective fading
Orthogonal Frequency Division Multiplexing (OFDM) technology has become widespread in civil and military radio systems, especially in channels with frequency selective fading. Due to the large number of OFDM signal schemes, an urgent task for modern radio monitoring systems is development of methods and algorithms for detecting such signals that will be stable in the uncertainty of OFDM signal structure and electromagnetic environment. At the stage of detection, the characteristic feature of OFDM signal is presence of frequency channels in its spectrum envelope. In this research, an algorithm for detecting an OFDM signal in the frequency domain and for estimating the number of frequency channels and duration of the interval of orthogonality was developed. To make a decision whether signal is present in realization of the normalized to the energy spectrum, its variation was used. This approach avoids estimating noise power. In case of signal samples detecting spectrum is double-smoothed using moving average. This provides better smoothing than with a single long window. Thereafter, double thresholding is performed. The second threshold is calculated using samples that have not exceeded the first threshold. Samples that have exceeded the second threshold are considered signal. Next, a search is made for occupied frequencies with a given bandwidth. The samples located in this band are re-smoothed and give spectrum trend, which is used as a threshold to determine the boundaries of frequency channels. OFDM signal is considered detected if equidistant frequency channels were found. After that, duration of the interval of orthogonality is calculated. The proposed method requires a slight complication of the spectral analysis procedure based on the fast Fourier transform. Proposed method can be used for improving broadband radio monitoring systems and provide practically simultaneously implementation procedure of OFDM signal detection-recognition.
Fazel K., Kaiser S. (2008). Multi-Carrier and Spread Spectrum Systems: From OFDM and MC-CDMA to LTE and WiMAX. 2nd Edition. John Wiley & Sons, Ltd, 380 p. DOI:10.1002/9780470714249.
Castro M. E. (2011). Cyclostationary detection for OFDM in cognitive radio systems. Theses, Dissertations, and Student Research from Electrical & Computer Engineering. University of Nebraska, 113 p.
Sohn S. H., Han N., Kim J. M. and Kim J. W. (2007). OFDM Signal Sensing Method Based on Cyclostationary Detection. 2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, pp. 63-68. doi: 10.1109/CROWNCOM.2007.4549773.
Gonzáles G., Cousseau J., Gregorio F., Wichman R., Werner S. (2011). A study of OFDM signal detection using cyclostationarity. ResearchGate, pp. 1-6.
Kim M., Po K., Takada J.-i. (2010). Performance Enhancement of Cyclostationarity Detector by Utilizing Multiple Cyclic Frequencies of OFDM Signals. 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN), pp. 1-8, doi: 10.1109/DYSPAN.2010.5457876.
Bixio L., Oliveri G., Ottonello M., Regazzoni C. S. (2009). OFDM Recognition Based on Cyclostationary Analysis in an Open Spectrum Scenario. VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, pp. 1-5. DOI: 10.1109/VETECS.2009.5073718.
Muzaffar M. U., El-Tarhuni M., Assaleh K. (2012). Learning-based Spectrum Sensing in OFDM Cognitive Radios. COCORA 2012. The Second International Conference on Advances in Cognitive Radio, pp. 57-62.
Vizziello A., Akyildiz I. F., Agusti R., Favalli L., Savazzi P. (2010). OFDM Signal Type Recognition and Adaptability Effects in Cognitive Radio Networks. 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, pp. 1-5. DOI: 10.1109/GLOCOM.2010.5683103.
Lushanur R. M. (2014). Study of the cyclostationarity properties of various signals of opportunity. Master of Science Thesis. Tampere university of technology, 119 p.
Noguet D., Biard L., Laugeois M. (2010). Cyclostationarity Detectors for Cognitive Radio: Architectural Tradeoffs. EURASIP Journal on Wireless Communications and Networking, Article number: 526429 (2010). doi:10.1155/2010/526429.
Le Nir, V., van Waterschoot, T., Moonen, M. et al. (2009). Blind CP-OFDM and ZP-OFDM Parameter Estimation in Frequency Selective Channels. EURASIP Journal on Wireless Communications and Networking, Article number: 315765 (2009). doi:10.1155/2009/315765.
Li H., Bar-Ness Y., Abdi A., Somekh O. S., Su W. (2006). OFDM Modulation Classification and Parameters Extraction. 2006 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications, pp. 1-6, doi: 10.1109/CROWNCOM.2006.363474.
Sun, Z., Chen, Y., Liu, S. et al. (2014). Cyclostationarity-based joint domain approach to blind recognition of SCLD and OFDM signals. EURASIP Journal on Advances in Signal Processing, Article number: 5 (2014). doi:10.1186/1687-6180-2014-5.
Tang, W., Cha, H., Wei, M., Tian, B., Ren, X. (2018). Identification method for OFDM signal based on fractal box dimension and pseudo-inverse spectrum. APSIPA Transactions on Signal and Information Processing, Vol. 7, E16. doi:10.1017/ATSIP.2018.19.
Karami E., Dobre O. A. (2018). Identification of SM-OFDM and AL-OFDM Signals Based on Their Second-Order Cyclostationarity. Cornell University, 36 p.
Haque M., Sugiura Y., Shimamura T. (2019). Spectrum Sensing Based on Higher Order Statistics for OFDM Systems over Multipath Fading Channels in Cognitive Radio. Journal of Signal Processing, Vol. 23, Iss. 6, pp. 257-266. DOI:10.2299/jsp.23.257.
Liedtke F., Albers U. (2008). Evaluation of features for the automatic recognition of OFDM signals in monitoring or cognitive receivers. Journal of telecommunications and information technology, pp. 30-36.
Buhаiov M. V. Analysis of high frequency OFDM modems signals in interest of radiomonitoring. (2020). Vceni zapiski Tavrijskogo nacionalnogo universitetu imeni V. I. Vernadskogo. Seria Tehnicni nauki, Vol. 31 (70), Iss. 5, pp. 30−35. DOI: 10.32838/2663-5941/2020.5/06. [In Ukrainian].
Recommendation ITU-R SM.1600-3. Technical identification of digital signals SM Series Spectrum management. (2017). ITU, 25 р.
Handbook. Spectrum monitoring. (2011). ITU, 678 р.
Buhaiov М. V. (2020). Iterative Method of Radiosignals Detection Based on Decision Statistics. Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, Vol. 81, pp. 11−20. doi: 10.20535/RADAP.2020.81.11-20. [In Ukrainian].
How to Cite
Copyright (c) 2021 Микола Вікторович Бугайов
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).