Sensing OFDM Signal: A Deep Learning Approach

Cheng, Q; Shi, Z; Nguyen, DN; Dutkiewicz, E

Sensing OFDM Signal: A Deep Learning Approach

Cheng, Q

Shi, Z Nguyen, DN

Dutkiewicz, E

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Communications, 2019, 67 (11), pp. 7785 - 7798
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Cheng, Q https://orcid.org/0000-0003-2664-5176	en_US
dc.contributor.author	Shi, Z	en_US
dc.contributor.author	Nguyen, DN https://orcid.org/0000-0003-2659-8648	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.date.accessioned	2020-04-17T01:10:55Z
dc.date.available	2019-09-01	en_US
dc.date.available	2020-04-17T01:10:55Z
dc.date.issued	2019-11-01	en_US
dc.identifier.citation	IEEE Transactions on Communications, 2019, 67 (11), pp. 7785 - 7798	en_US
dc.identifier.issn	0090-6778	en_US
dc.identifier.uri	http://hdl.handle.net/10453/140061
dc.description.abstract	© 1972-2012 IEEE. Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising technology to address the radio spectrum shortage. In particular, sensing of orthogonal frequency division multiplexing (OFDM) signals, a widely accepted multi-carrier transmission paradigm, has received paramount interest. Despite various efforts, noise uncertainty, timing delay and carrier frequency offset (CFO) still remain as challenging problems, significantly degrading the sensing performance. In this work, we develop two novel OFDM sensing frameworks utilizing the properties of deep learning networks. Specifically, we first propose a stacked autoencoder based spectrum sensing method (SAE-SS), in which a stacked autoencoder network is designed to extract the hidden features of OFDM signals for classifying the user's activities. Compared to the conventional OFDM sensing methods, SAE-SS is significantly superior in the robustness to noise uncertainty, timing delay, and CFO. Moreover, SAE-SS requires neither any prior information of signals (e.g., signal structure, pilot tones, cyclic prefix) nor explicit feature extraction algorithms which however are essential for the conventional OFDM sensing methods. To further improve the sensing accuracy of SAE-SS, especially under low SNR conditions, we propose a stacked autoencoder based spectrum sensing method using time-frequency domain signals (SAE-TF). SAE-TF achieves higher sensing accuracy than SAE-SS using the features extracted from both time and frequency domains, at the cost of higher computational complexity. Through extensive simulation results, both SAE-SS and SAE-TF are shown to achieve notably higher sensing accuracy than that of state of the art approaches.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE150101092
dc.relation.ispartof	IEEE Transactions on Communications	en_US
dc.relation.isbasedon	10.1109/TCOMM.2019.2940013	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Sensing OFDM Signal: A Deep Learning Approach	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	67	en_US
utslib.for	0804 Data Format	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	67	en_US

Abstract:

© 1972-2012 IEEE. Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising technology to address the radio spectrum shortage. In particular, sensing of orthogonal frequency division multiplexing (OFDM) signals, a widely accepted multi-carrier transmission paradigm, has received paramount interest. Despite various efforts, noise uncertainty, timing delay and carrier frequency offset (CFO) still remain as challenging problems, significantly degrading the sensing performance. In this work, we develop two novel OFDM sensing frameworks utilizing the properties of deep learning networks. Specifically, we first propose a stacked autoencoder based spectrum sensing method (SAE-SS), in which a stacked autoencoder network is designed to extract the hidden features of OFDM signals for classifying the user's activities. Compared to the conventional OFDM sensing methods, SAE-SS is significantly superior in the robustness to noise uncertainty, timing delay, and CFO. Moreover, SAE-SS requires neither any prior information of signals (e.g., signal structure, pilot tones, cyclic prefix) nor explicit feature extraction algorithms which however are essential for the conventional OFDM sensing methods. To further improve the sensing accuracy of SAE-SS, especially under low SNR conditions, we propose a stacked autoencoder based spectrum sensing method using time-frequency domain signals (SAE-TF). SAE-TF achieves higher sensing accuracy than SAE-SS using the features extracted from both time and frequency domains, at the cost of higher computational complexity. Through extensive simulation results, both SAE-SS and SAE-TF are shown to achieve notably higher sensing accuracy than that of state of the art approaches.

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http://hdl.handle.net/10453/140061