Detection of Interfering Signals and Estimation of Their Carrier Frequency in CNC Satellite Communications using Cyclic Spectrum Density

Document Type : Original Article

Authors

1 PhD student, Imam Hossein University (AS), Tehran, Iran

2 Assistant Professor, Imam Hossein University, Tehran, Iran

3 Assistant Professor, Communication and Information Technology Research Institute, Tehran, Iran

Abstract

Satellite communication is considered a significant part of the enemy's communication information in electronic warfare due to its unique features and widespread use in communication systems. Therefore, from the electronic support (ES) perspective, monitoring ability and identifying and analyzing enemy satellite network communication are very important. However, the new CNC technology in satellite communication has challenged the detection and analysis of the communication signal based on this technology in non-cooperative receivers, due to the nature of time-frequency overlaps. So far, no method for detecting the presence of interfering signals has been presented in open scientific literature. In this paper, the statistical cyclostationary properties of communication signals are used as a new method of detecting in-band interference in CNC satellite communication. To achieve this goal, first, the cyclic autocorrelation function for interfering signals is calculated, and mathematical equations of cyclic power spectrum density function are developed for interfering signals with less computational complexity. Then, using periodicity statistical properties of signals, in-band interference will be detected and the carrier frequencies of each interfering signal are also estimated. The results of the simulations show that the probability of correctly identifying the interference and estimating the carrier frequency in the time-frequency interference of two signals with BPSK and QPSK modulations is different. In BPSK modulation, the probability from the signal-to-noise ratio of -10dB is constant and around 98%, but in QPSK modulation, it increases from the signal-to-noise ratio of 0dB and reaches 80% in the signal-to-noise ratio of 35dB.

Keywords

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References

[1]    Y. Yang, H. Peng, D. Zhang and X. Dai, "Markov Chain Monte Carlo-Based Separation of Paired Carrier Multiple Access Signals," IEEE Communications Letters , vol. 20, no. 11, pp. 2209 - 2212, 2016. 
[2]    S. Preethi and k.Rajeswari, "A Survey on Multiple Access Techniques for Mobile communication," International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol. 1, pp. 1-5, 2015. 
[3]    C. Agne, M. B. Cornell, M. Dale, R. Kearns and F. Lee, "Shared-spectrum bandwidth efficient satellite communications," in 2010 - MILCOM 2010 MILITARY COMMUNICATIONS CONFERENCE, San Jose, CA, USA, 2010. 
[4]    Y. Guo, H. Peng and J. Fu, "Joint Blind Parameter Estimation of Non-cooperative High-Order Modulated PCMA Signals," KSII Transactions on Internet and Information Systems (TIIS), vol. 12, no. 10, pp. 4873-4888 , 2018. 
[5]    T. V. R. O. Câmara, A. D. L. Lima, B. M. M. Lima, A. I. R. Fontes, A. D. M. Martins and L. F. Q. Silveira, "Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments," IEEE Access, vol. 7, pp. 138512 - 138527, 2019. 
[6]    M. Saadatmand and M. R. Khair, "Identification of STBC-OFDM Signals by Second-Order Cyclostationarity Method," Journal of Electronical & Cyber Defence, vol. 7, no. 4, pp. 27-35, 2020. 
[7]    Y. Liu, Y. Tie, S. Na and S. Tan, "Carrier Frequency Estimation with Cyclostationary Signals in Impulsive Noise," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 9, no. 6, pp. 89-102, 2016. 
[8]    C. Sisi and Z. Weiyan, "Carrier frequency and symbol rate estimation based on cyclic spectrum," Journal of Systems Engineering and Electronics, vol. 31, no. 1, pp. 37-44, February 2020. 
[9]    T. Li, Y. Li and O. A. Dobre, "Modulation Classification Based on Fourth-Order Cumulants of Superposed Signal in NOMA Systems," IEEE Transactions on Information Forensics and Security, vol. 16, pp. 2885 - 2897, 2021 
[10]         M. SIMIC, M. STANKOVIC and V. D. ORLIC, "Automatic Modulation Classification of Real Signals in AWGN Channel Based on Sixth-Order Cumulants," Radioengineering, vol. 2021, pp. 204-214, 2021.
[11]         Y. Yong, Z. Dongling and P. Hua, "Single-Channel Blind Source Separation for Paired Carrier Multiple Access Signal," IET Signal Processing, vol. 12, no. 1, pp. 37-41, 2017. 
[12]         X. Liu, Y. L. Guan, S. N. Koh, Z. Liu and P. Wang, "Low-Complexity Single-Channel Blind Separation of Co-Frequency Coded Signals," IEEE Communications Letters, vol. 22, no. 5, pp. 990 - 993, 2018. 
[13]         W. Chi, P. Hua and F. Junhui, "A Blind Separation Method of PCMA Signals Based on MSGibbs Algorithm," ingle-channel Demodulation Algorithm for, vol. 1168, no. 5, 2018. 
[14]         C. Wei, H. Peng and J. Fan, "Single-channel Demodulation Algorithm for Non-cooperative PCMA Signals Based on Neural Network," KSII Transactions on Internet and Information Systems, vol. 13, no. 7, pp. 3433-3446, 2019. 
[15]         A. Napolitano, "Generalizations of Cyclostationarity: A New Paradigm for Signal Processing for Mobile Communications, Radar, and Sonar," IEEE Signal Processing Magazine , vol. 30, no. 6, pp. 53 - 63, 2013. 
[16]         S. Vukotić, "Detection and clasiffication of OFDM/QAM and OFDM/OQAM signals based on cyclostationary features," in 2015 23rd Telecommunications Forum Telfor (TELFOR), Belgrade, Serbia, 24-26 Nov. 2015. 
[17]         J. Wang, R. Gao, D. Ye and Z. Zhang, "Blind detection of cyclostationary signals based on multi-antenna beamforming technology," IET Communications, vol. 15, pp. 2439-2447, 2021
[18]         W. A. Gardner, Cyclostationarity in Communications and Signal, New York: IEEE Communication Society, 1994. 
[19]         J. Antoni, G. Xin and N. Hamzaoui, "Fast computation of the spectral correlation," Mechanical Systems and Signal Processing,, vol. 92, p. 2017, 248-277. 
[20]         W. M. Jang, "Blind Cyclostationary Spectrum Sensing in Cognitive Radios," IEEE Communications Letters , vol. 18, no. 3, pp. 393 - 396, 2014. 
[21]         J. L. B. Alvarez and F. E. H. Montero, "Classification of MPSK Signals through Eighth-Order Statistical Signal Processing," IEEE Latin America Transactions, vol. 15, no. 9, pp. 1601 - 1607, 2017. 
[22]         X. Yang, S. Li and F. Li, "Fourth-order nonlinear distortion to the power spectrum of RF amplifiers," The Journal of Engineering, vol. 2022, no. 1, pp. 53-63, 2021.
Electronic and Cyber Defense
Volume 11, Issue 2 - Serial Number 42
No. 42, Summer
July 2023
Pages 91-101

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History

  • Receive Date: 10 September 2022
  • Revise Date: 28 January 2023
  • Accept Date: 17 May 2023
  • Publish Date: 22 June 2023

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