آشکار سازی کور سیگنال‌های برست در کانال گوسی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشگاه صنعتی مالک اشتر

چکیده

امروزه طراحی یک گیرنده هوشمند به­منظور شناسایی و آشکارسازی کور سیگنال در ارتباطات مبتنی بر سیگنال برست بسیار ضروری است. در این نوع گیرنده‌ها اولین و مهم‌ترین گام آشکارسازی، تشخیص حضور سیگنال و تعیین نقاط ابتدایی و انتهایی برست­ها به‌منظور استخراج اطلاعات است. در این مقاله، روش جدیدی برای آشکارسازی کور سیگنال برست ارائه می­گردد. در روش پیشنهادی نقاط ابتدا و انتهای برست­­ها که درواقع لبه‌های سیگنال هستند با روش تبدیل موجک به‌دست می­آیند. معیار تشابه دایس، معیار خطای برست، معیار خطای زمان سکوت و معیار خطای کل برای بررسی عملکرد آشکارساز ارائه ‌شده است. نتایج حاصل از شبیه‌سازی‌ آشکارساز تبدیل موجک در کانال گوسی به‌عنوان نمونه برای طول برست­های بلند، ضریب دایس در سیگنال به نویز dB2-  به 9768/0 می­رسد که نشان‌دهنده عملکرد مناسب روش پیشنهادی در مقایسه با روش­های موجود در این زمینه است.

کلیدواژه‌ها


عنوان مقاله [English]

Blind Detection of Burst Signals in the Gaussian Channel

نویسندگان [English]

  • V. Hayati
  • H. Khaleghi
  • O. Pakdel
Malek Ashtar University of Technology
چکیده [English]

Today, the design of an intelligent receiver is essential for identification and blind detection of signals in the communications that are based on the burst signal. The first and most important step in this type of receiver detection, is signal presence detection and determination of the start and end points of the bursts for the purpose of extracting information. In this paper, a new method for blind detection of the burst signal is provided. In the proposed method, the beginning and end of bursts, which are actually the edges of the signal, are obtained by wavelet transform. Dice similarity criterion, burst error criterion, silence time error criterion and total error criterion are applied to investigate the detector performance. For long bursts taken as an example, the results of the wavelet transform detector simulation in the Gaussian channel, indicate that in the signal to noise ratio of -2 dB, the dice coefficient is around 0/9768 which shows the proper performance of the proposed method compared to the existing methods in this field.

کلیدواژه‌ها [English]

  • Burst Signal
  • Blind Detection
  • Gaussian Channel
  • Short and Long Burst
  • Wavelet Transform

Smiley face

[1]     M. Teimouri and H. Kakaei Motlagh, “Reverse engineering of communications networks: evolution and challenges,” arXiv preprint arXiv: 1704.05432, 2017.##
[2]     M. Zebarjadi and M. Teimouri, “Blind Detection of Burst Signals in Non-Cooperative Environment,” Tabriz Journal of Eelctrical Engineering, vol. 47, pp. 1465-1477, 2018.##
[3]     Dan, Sui, X.  Xiaojian, and W. Jing, “A novel presence detector for burst signals based on the fluctuation of the correlation function,” 10th IEEE Internationa Conference on Signal Processing Prodeedings, 2010.##
[4]     Y. Zeng and Y. C. Liang, “Spectrum-sensing algorithms for cognitive radio based on statistical covariances,” IEEE transactions on Vehicular Technology, vol. 58, pp. 1804-1815, 2008.##
[5]     F. F. Digham, M. S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” IEEE International Conference on Communications, 2003.##
[6]     A. Bagheri, et al, “Energy detection based spectrum sensing over enriched multipath fading channels,” In IEEE Wireless Communications and Networking Conference, 2016.##
[7]     E. Chatziantoniou, et al, “Energy detection based spectrum sensing over two-wave with diffuse power fading channels,” IEEE Transactions on Vehicular Technology, vol. 66, pp. 868-874, 2016.##
[8]     P. Avinash, R. Gandhiraj, and K. P. Soman, “Spectrum sensing using compressed sensing techniques for sparse multiband signals,” International Journal of Scientific & Engineering Research, vol. 3, pp. 1-5, 2012.##
[9]     M. R. Manesh, et al, “Real-time spectrum occupancy monitoring using a probabilistic model,” Computer Networks, vol. 124, pp. 87-96, 2017.##
[10]  F. Salahdine, et al, “Matched filter detection with dynamic threshold for cognitive radio networks,” IEEEInternational Conference on Wireless Networks and Mobile Communications, 2015.##
[11]  X. Zhang, X. Liu, H. Samani, and B. Jalaian, “Cooperative spectrum sensing in cognitive wireless sensor networks,” International Journal of Distributed Sensor Networks, vol. 11, pp. 1077–1092, 2015.##
[12]  H. Oh and H. Nam, “Energy detection scheme in the presence of burst signals,” IEEE Signal Processing Letters, vol. 26, pp. 582-586, 2019.##
[13]  M. Zebarjadi and M. Teimouri, “Non-cooperative burst detection and synchronisation in downlink TDMA-based wireless communication networks,” IET Communications, vol. 13, pp. 863-872, 2019.##
[14]  C. Bektas, A. Akan, and N. Odabasioglu, “Energy based spectrum sensing using wavelet transform for fading channels,” IEEE International Congress on Ultra Modern Telecommunications and Control Systems, 2012.##
[15]  K. Kuzume and T. Tabusa, “Dyadic lifting wavelet based signal detection,” 3rd IEEE International Conference on Artificial Intelligence and Pattern Recognition, 2016.##
[16]  M. Teimouri, H. R. Kakaei Motlagh, and J. Garshasbi, “Blind Identification of Communications Networks in Service Layer,” Passive Defense journal, vol. 10, pp.  91-101, 202.##