Blind Modulation Classification of Wi-Fi 6 and 5G signals for Spectrum Sensing
Pages 137 - 145
Abstract
Classification of modulation of Wi-Fi~6 and 5G downlink (DL) user data signals for spectrum sensing is studied. First, the orthogonal frequency division multiplexing (OFDM) symbol duration and cyclic prefix (CP) length are estimated based on the cyclic autocorrelation function (CAF). We propose a feature extraction algorithm characterizing the modulation of OFDM signals based on the estimated parameters. The algorithm includes removing the effects of a synchronization error and converting the obtained feature into a 2D histogram of phase and amplitude. This histogram is input to a convolutional neural network (CNN)-based classifier. Our system works without knowledge of a carrier frequency, Wi-Fi preamble, or resource allocation of 5G physical channels. We evaluate the classifier's performance with data with various protocol-compliant configurations. Our classifier achieves at least 98% accuracy when SNR is above the value required for data transmission.
References
[1]
3GPP TR 38.141--1. 2023. NR; Radio Resource Control (RRC); Base Station (BS) conformance testing; Part 1: Conducted conformance testing. ver. 18.1.0.
[2]
3GPP TR 38.331. 2023. NR; Radio Resource Control (RRC); Protocol specification. ver 17.4.0.
[3]
3GPP TR 38.521--4. 2023. NR; User Equipment (UE) conformance specification; Radio transmission and reception; Part 4: Performance requirements. ver 17.2.1.
[4]
Dhamyaa Husam Al-Nuaimi, Nor Ashidi Mat Isa, Muhammad Firdaus Akbar, and Intan Sorfina Zainal Abidin. 2021. AMC2-Pyramid: Intelligent pyramidal feature engineering and multi-Distance decision making for automatic multi-carrier modulation classification. IEEE Access, Vol. 9 (Sept. 2021), 137560--137583. https://doi.org/10.1109/ACCESS.2021.3115888
[5]
Nicola Bui and Joerg Widmer. 2016. OWL: A reliable online watcher for LTE control channel measurements. In Proc. 5th Workshop on All Things Cellular: Operations, Applications and Challenges. Association for Computing Machinery, New York (NY), USA, 25--30. https://doi.org/10.1145/2980055.2980057
[6]
Robert Falkenberg and Christian Wietfeld. 2019. FALCON: An accurate real-time monitor for client-based mobile network data analytics. In Proc. IEEE GLOBECOM. IEEE, Waikoloa (HI), USA, 1--7. https://doi.org/10.1109/GLOBECOM38437.2019.9014096
[7]
Jiabao Gao, Xuemei Yi, Caijun Zhong, Xiaoming Chen, and Zhaoyang Zhang. 2019. Deep learning for spectrum sensing. IEEE Wireless Commun. Letters, Vol. 8, 6 (2019), 1727--1730. https://doi.org/10.1109/LWC.2019.2939314
[8]
William A Gardner and Chad M Spooner. 1994. The cumulant theory of cyclostationary time-series. I. Foundation. IEEE Trans. Signal Process., Vol. 42, 12 (Dec. 1994), 3387--3408. https://doi.org/10.1109/78.340775
[9]
Rahul Gupta, Sushant Kumar, and Sudhan Majhi. 2020. Blind modulation classification for asynchronous OFDM systems over unknown signal parameters and channel statistics. IEEE Trans. Veh. Technol., Vol. 69, 5 (Mar. 2020), 5281--5292. https://doi.org/10.1109/TVT.2020.2981935
[10]
Tuan Dinh Hoang, CheolJun Park, Mincheol Son, Taekkyung Oh, Sangwook Bae, Junho Ahn, Beomseok Oh, and Yongdae Kim. 2023. LTESniffer: An open-source LTE downlink/uplink eavesdropper. In Proc. 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec). Guildford, UK, 43----48. https://doi.org/10.1145/3558482.3590196
[11]
Sheng Hong, Yu Wang, Yuwen Pan, Hao Gu, Miao Liu, Jie Yang, and Guan Gui. 2020. Convolutional neural network aided signal modulation recognition in OFDM systems. In Proc. IEEE VTC. 1--5. https://doi.org/10.1109/VTC2020-Spring48590.2020.9128455
[12]
Sheng Hong, Yibin Zhang, Yu Wang, Hao Gu, Guan Gui, and Hikmet Sari. 2019. Deep learning-based signal modulation identification in OFDM systems. IEEE Access, Vol. 7 (Aug. 2019), 114631--114638. https://doi.org/10.1109/ACCESS.2021.3102223
[13]
Steven Siying Hong and Sachin Rajsekhar Katti. 2011. DOF: A local wireless information plane. In Proc. ACM SIGCOMM. 230--241. https://doi.org/10.1145/2018436.2018463
[14]
IEEE 802.11ax. 2021. Part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 1: enhancements for high-efficiency WLAN.
[15]
IEEE 802.11n. 2009. Part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 5: enhancements for higher throughput.
[16]
Anand Kumar, Keerthi Kumar Srinivas, and Sudhan Majhi. 2023. Automatic Modulation Classification for Adaptive OFDM Systems Using Convolutional Neural Networks with Residual Learning. IEEE Access, Vol. 11 (Jun. 2023), 61013--61024. https://doi.org/10.1109/ACCESS.2023.3286939
[17]
Swarun Kumar, Ezzeldin Hamed, Dina Katabi, and Li Erran Li. 2014. LTE radio analytics made easy and accessible. Proc. ACM SIGCOMM, Vol. 44, 4 (Aug. 2014), 211--222. https://doi.org/10.1145/2740070.2626320
[18]
Yan Li, Johan Barthelemy, Shuai Sun, Pascal Perez, and Bill Moran. 2020. A case study of WiFi sniffing performance evaluation. IEEE Access, Vol. 8 (2020), 129224--129235. https://doi.org/10.1109/ACCESS.2020.3008533
[19]
Dancheng Liu, Kazim Ergun, and Tajana vS imunić Rosing. 2023. Towards a Robust and Efficient Classifier for Real World Radio Signal Modulation Classification. In Proc. IEEE ICASSP. 1--5. https://doi.org/10.1109/ICASSP49357.2023.10094907
[20]
Golledge Electronics Ltd. 2023. GTXO-203T textbar 1.8V$sim$3.6V SM TCXO textbar Golledge. https://www.golledge.com/products/gtxo-203t-ultra-miniature-tight-stability-tcxo/c-26/p-287/ Retrieved 06.21.2023 from
[21]
Norbert Ludant, Pieter Robyns, and Guevara Noubir. 2023. From 5G Sniffing to Harvesting Leakages of Privacy-Preserving Messengers. In 2023 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, Los Alamitos, CA, USA, 1919--1934. https://doi.org/10.1109/SP46215.2023.00110
[22]
MathWorks. 2023. MATLAB Products. https://www.mathworks.com/products.html/ Retrieved 06.21.2023 from
[23]
Myung Chul Park and Dong Seog Han. 2021. Deep learning-based automatic modulation classification with blind OFDM parameter estimation. IEEE Access, Vol. 9 (2021), 108305--108317. https://doi.org/10.1109/ACCESS.2021.3102223
[24]
Amit Kumar Pathy, Anand Kumar, Rahul Gupta, Sushant Kumar, and Sudhan Majhi. 2021. Design and implementation of blind modulation classification for asynchronous MIMO-OFDM system. IEEE Trans. Instrum. Meas., Vol. 70 (Sept. 2021), 1--11. https://doi.org/10.1109/TIM.2021.3109737
[25]
Anjana Punchihewa, Vijay K Bhargava, and Charles Despins. 2011. Blind estimation of OFDM parameters in cognitive radio networks. IEEE Trans. Wireless Commun., Vol. 10, 3 (Mar. 2011), 733--738. https://doi.org/10.1109/TWC.2010.010411.100276
[26]
Rishad Ahmed Shafik, Md Shahriar Rahman, and AHM Razibul Islam. 2006. On the extended relationships among EVM, BER and SNR as performance metrics. In Proc. IEEE ICECE. 408--411. https://doi.org/10.1109/ICECE.2006.355657
[27]
Ling Yu, Jin Chen, and Guoru Ding. 2017. Spectrum prediction via long short term memory. In Proc. IEEE ICCC. 643--647.
[28]
Zufan Zhang, Hao Luo, Chun Wang, Chenquan Gan, and Yong Xiang. 2020. Automatic modulation classification using CNN-LSTM based dual-stream structure. IEEE Trans. Veh. Technol., Vol. 69, 11 (Nov. 2020), 13521--13531. io
Index Terms
- Blind Modulation Classification of Wi-Fi 6 and 5G signals for Spectrum Sensing
Recommendations
Blind Signal Modulation Recognition through Density Spread of Constellation Signature
AbstractAutomatic recognition of modulation scheme in blind environment plays a key role in many communication applications. A hierarchical and local density (HLD) approach is proposed to classify eight modulation schemes in a two stage process. In the ...
Spectrum sensing for OFDM systems employing pilot tones
OFDM spectrum sensing algorithms are presented in this paper. The proposed spectrum sensing algorithms are based on Time-Domain Symbol Cross-Correlation (TDSC-MRC and TDSC-NP methods) and can be applied to all existing wireless OFDM systems. The ...
Robust spectrum sensing for orthogonal frequency division multiplexing signal without synchronization and prior noise knowledge
Spectrum sensing is defined as the task of detecting the presence of licensed users and is an essential prerequisite for opportunistic spectrum access in cognitive radio. Motivated by the infeasible assumptions of perfect synchronization and prior noise ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2023
330 pages
ISBN:9798400703669
DOI:10.1145/3616388
- General Chair:
- Azzedine Boukerche,
- Program Chairs:
- Floriano De Rango,
- Zhi Sun
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 October 2023
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
MSWiM '23
Sponsor:
MSWiM '23: Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems
October 30 - November 3, 2023
Quebec, Montreal, Canada
Acceptance Rates
Overall Acceptance Rate 398 of 1,577 submissions, 25%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 186Total Downloads
- Downloads (Last 12 months)186
- Downloads (Last 6 weeks)21
Reflects downloads up to 26 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in