Methods for the automatic recognition of digital modulation of signals in cognitive radio systems

This paper considers one of the problematic issues of creating radio systems based on cognitive radio technology, viz., automatic recognition of the digital-modulation formats of radio signals. In accordance with the recommendations of the E2R and the European Telecommunications Standards Institute (ETSI) consortium, cognitive radio systems have the ability to modulate/demodulate signals in all frequency bands and in all modes of modulation. This process should be performed automatically, according to the current technical capabilities of the available communication system, the requirements for the quality of communication, and different external conditions. This article provides an analysis of the promising methods of automatic recognition of digitally modulated radio signal formats, viz., using the shape of the phase constellation, using the distribution difference of instantaneous phases, and using high-order cumulants. According to the results of the analysis, we propose methods of recognition that are based on cumulant analysis for cognitive radio systems. It is proposed that the decision-making device be an artificial neural network.

[1]  Yasir Saleem,et al.  A survey on network coding: From traditional wireless networks to emerging cognitive radio networks , 2014, J. Netw. Comput. Appl..

[2]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[3]  D. S. Holmes,et al.  Energy-Efficient Superconducting Computing—Power Budgets and Requirements , 2013, IEEE Transactions on Applied Superconductivity.

[4]  Rama Murthy Garimella,et al.  Towards Faster Spectrum Sensing Techniques in Cognitive Radio Architectures , 2015 .

[5]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[6]  Asoke K. Nandi,et al.  Automatic Modulation Recognition of Communication Signals , 1996 .

[7]  Elsayed Elsayed Azzouz,et al.  Automatic identification of digital modulation types , 1995, Signal Process..

[8]  Daryoush Habibi,et al.  Classification of digital modulation schemes using neural networks , 1999, ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359).

[9]  Chang-Tien Lu,et al.  SpecMonitor: Toward Efficient Passive Traffic Monitoring for Cognitive Radio Networks , 2014, IEEE Transactions on Wireless Communications.

[10]  Vladimir Dotsenko,et al.  Invited Paper Special Section on Recent Progress in Superconductive Digital Electronics Superconductor Digital-rf Receiver Systems , 2022 .

[11]  Ali Mansour,et al.  Automatic modulation recognition of MPSK signals using constellation rotation and its 4th order cumulant , 2005, Digit. Signal Process..

[12]  Oleg A. Mukhanov,et al.  Superconductor analog-to-digital converters , 2004, Proceedings of the IEEE.

[13]  Bijan G. Mobasseri,et al.  Digital modulation classification using constellation shape , 2000, Signal Process..

[14]  D. S. Chirov,et al.  Type recognition of the digital modulation of radio signals using neural networks , 2015 .

[15]  M.P. Fargues,et al.  A hierarchical approach to the classification of digital modulation types in multipath environments , 2001, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[16]  O. Mukhanov,et al.  Superconductivity and the environment: a Roadmap , 2013 .

[17]  Brian M. Sadler,et al.  Hierarchical digital modulation classification using cumulants , 2000, IEEE Trans. Commun..