Cognitive Radio: From Spectrum Sharing to Adaptive Learning and Reconfiguration

This paper introduces important cognitive radio developments like spectrum sharing, learning and adaptation algorithms, and the software and hardware architecture to support these functions. A cognitive radio is defined here as a transceiver that is aware of its environment and can combine this awareness with knowledge of its user's priorities, needs, operational procedures, and governing regulatory rules. It adapts to its environment and configures itself in an appropriate fashion. The radio learns through experience and is capable of generating solutions for communications problems unforeseen by its designers. Our spectrum sharing cognitive radio is built upon GNU radio and uses the universal software radio peripheral (USRP) device as our radio front end platform. We use cyclostationary feature analysis to detect low SNR modulated signals because of its ability to distinguish between modulated signals, interference, and noise in low signal to noise ratios. A parallel algorithm running on a cell broadband engine (Cell BE) is used to attack the associated high computational complexity. A new spectrum sensing scheme, incorporating spectrum monitoring, data transmission, and dynamic channel switching, is designed to fully utilize the idle time of the primary user. Our work is based on the concept of a cognitive engine: an intelligent software package that "reads the meters" and "turns the knobs" of any attached software defined radio (SDR) platform. Using an eclectic combination of artificial intelligence techniques including case-based decision theory, multi-objective genetic algorithms, and neural networks, it implements a system of nested cognition loops. Applied to public safety communications, this technology is the basis of a working prototype Public Safety Cognitive Radio that can scan the public safety spectrum (multiple bands and multiple waveforms, all incompatible) and configure itself to interoperate with any public safety waveform that it finds within 0.1 seconds of determining that a signal is present.

[1]  Charles W. Bostian,et al.  MODULATION IDENTIFICATION USING NEURAL NETWORKS FOR COGNITIVE RADIOS , 2005 .

[2]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[3]  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..

[4]  Joseph Mitola,et al.  Software Radio Architecture: Object-Oriented Approaches to Wireless Systems Engineering , 2000 .

[5]  W. A. Brown,et al.  Computationally efficient algorithms for cyclic spectral analysis , 1991, IEEE Signal Processing Magazine.

[6]  K.N. Steadman,et al.  Dynamic Spectrum Sharing Detectors , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[7]  Bin Le,et al.  Building a Cognitive Radio: From Architecture Definition to Prototype Implementation , 2007 .

[8]  Jerry Brito.  Sending Out an S.O.S.: Public Safety Communications Interoperability as a Collective Action Problem , 2007 .

[9]  Charles W. Bostian,et al.  Application of artificial intelligence to wireless communications , 2007 .

[10]  Qinqin Chen,et al.  A PUBLIC SAFETY COGNITIVE RADIO NODE , 2007 .

[11]  Jeffrey H. Reed.  Software Radio , 2002 .

[12]  N. J. Carter,et al.  Implementation of Cyclic Spectral Analysis Methods , 1992 .

[13]  Peter J. Fleming,et al.  Evolutionary many-objective optimisation: an exploratory analysis , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[14]  Scott F. Midkiff,et al.  Cognitive Radios in Public Safety and Spectrum Management , 2005 .

[15]  Jeffrey H. Reed,et al.  Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.