Cognitive Radio as the Facilitator for Advanced Communications Electronic Warfare Solutions

Throughout the 1990s, Software Defined Radio (SDR) technology was viewed almost exclusively as a solution for interoperability problems between various military standards, waveforms and devices. In the meantime, Cognitive Radio (CR) – a novel communication paradigm which embodies SDR with intelligence and self-reconfigurability properties – has emerged. Intelligence and on-the-fly self-reconfiguration abilities of CRs constitute an important next step in the Communications Electronic Warfare, as they may enable the jamming entities with the capabilities of devising and deploying advanced jamming tactics. Similarly, they may also aid the development of the advanced intelligent self-reconfigurable systems for jamming mitigation. This work outlines the development and implementation of the Spectrum Intelligence algorithm for Radio Frequency (RF) interference mitigation. The developed system is built upon the ideas of obtaining relevant spectrum-related data by using wideband energy detectors, performing narrowband waveform identification, extracting the waveforms’ parameters and properly classifying the waveforms. All relevant spectrum activities are continuously monitored and stored. Coupled with the self-reconfigurability of various transmission-related parameters, Spectrum Intelligence is the facilitator for the advanced interference mitigation strategies. The implementation is done on the Cognitive Radio test bed architecture which consists of two military Software Defined Radio terminals, each interconnected with the computationally powerful System-on-Module.

[1]  H. L. Hirsch Statistical signal characterization-new help for real-time processing , 1992, Proceedings of the IEEE 1992 National Aerospace and Electronics Conference@m_NAECON 1992.

[2]  J. Mitola,et al.  Software radios: Survey, critical evaluation and future directions , 1992, IEEE Aerospace and Electronic Systems Magazine.

[3]  Ping Feng,et al.  Spectrum-blind minimum-rate sampling and reconstruction of multiband signals , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[4]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[5]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[6]  Richard A. Poisel,et al.  Introduction to Communication Electronic Warfare Systems , 2002 .

[7]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[8]  R.W. Brodersen,et al.  Implementation issues in spectrum sensing for cognitive radios , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[9]  Richard G. Baraniuk,et al.  Fast reconstruction of piecewise smooth signals from random projections , 2005 .

[10]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[11]  S. Kirolos,et al.  Random Sampling for Analog-to-Information Conversion of Wideband Signals , 2006, 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software.

[12]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[13]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[14]  R.W. Brodersen,et al.  Cyclostationary Feature Detector Experiments Using Reconfigurable BEE2 , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[15]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[16]  Brian M. Sadler,et al.  Mixed-signal parallel compressed sensing and reception for cognitive radio , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Ghanshyam Singh,et al.  Opportunistic Spectrum Sensing by Employing Matched Filter in Cognitive Radio Network , 2011, 2011 International Conference on Communication Systems and Network Technologies.

[18]  Chen Li,et al.  Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks , 2011, IEEE Journal of Selected Topics in Signal Processing.

[19]  R. Fantacci,et al.  PERFORMANCE EVALUATION OF A SPECTRUM-SENSING TECHNIQUE FOR LDACS AND JTIDS COEXISTENCE IN L-BAND , 2012 .

[20]  Tuna Tugcu,et al.  Radio environment map as enabler for practical cognitive radio networks , 2013, IEEE Communications Magazine.

[21]  Carlo S. Regazzoni,et al.  SPD-driven Smart Transmission Layer based on a Software Defined Radio Test Bed Architecture , 2014, PECCS.

[22]  C. S. Regazzoni,et al.  Experimental Study of Spectrum Estimation and Reconstruction based on Compressive Sampling for Cognitive Radios , 2014 .

[23]  Alejandro Betancourt,et al.  A fictitious play-based game-theoretical approach to alleviating jamming attacks for cognitive radios , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  Carlo S. Regazzoni,et al.  SPECTRUM INTELLIGENCE FOR INTERFERENCE MITIGATION FOR COGNITIVE RADIO TERMINALS , 2014 .

[25]  Carlo S. Regazzoni,et al.  Energy Detection in Multihop Cooperative Diversity Networks: An Analytical Study , 2014, Int. J. Distributed Sens. Networks.

[26]  Alejandro Betancourt,et al.  Intelligent cognitive radio jamming - a game-theoretical approach , 2014, EURASIP J. Adv. Signal Process..