Cognitive radio: brain-empowered wireless communications

Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.

[1]  Jimmy Xu,et al.  Nanotube electronics: non-CMOS routes , 2003 .

[2]  Fredrik Gustafsson,et al.  Adaptive filtering and change detection , 2000 .

[3]  L. Rayleigh.  XXI. On the spectrum of an irregular disturbance , 1903 .

[4]  S. Haykin,et al.  Modern Wireless Communications , 1939, Nature.

[5]  R. Aumann,et al.  Epistemic Conditions for Nash Equilibrium , 1995 .

[6]  M. A. Stuchly,et al.  Engineering Issues in Space Weather , 1999 .

[7]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[8]  P. K. Chaturvedi,et al.  Communication Systems , 2002, IFIP — The International Federation for Information Processing.

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

[10]  D. Thomson.  Spectrum estimation techniques for characterization and development of WT4 waveguide–I , 1977, The Bell System Technical Journal.

[11]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[12]  H. Pollak,et al.  Prolate spheroidal wave functions, fourier analysis and uncertainty — III: The dimension of the space of essentially time- and band-limited signals , 1962 .

[13]  Lizhong Zheng,et al.  Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels , 2003, IEEE Trans. Inf. Theory.

[14]  M. J. Gans,et al.  On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas , 1998, Wirel. Pers. Commun..

[15]  Daniel L. Stein,et al.  Lectures In The Sciences Of Complexity , 1989 .

[16]  R. T. Compton.  Adaptive Antennas: Concepts and Performance , 1988 .

[17]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[18]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[19]  Simon Haykin,et al.  Bayesian sequential state estimation for MIMO wireless communications , 2004, Proceedings of the IEEE.

[20]  G. Staple,et al.  The end of spectrum scarcity [spectrum allocation and utilization] , 2004, IEEE Spectrum.

[21]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[22]  Avrim Blum,et al.  Planning in the Presence of Cost Functions Controlled by an Adversary , 2003, ICML.

[23]  J M Smith,et al.  Evolution and the theory of games , 1976 .

[24]  I. Prigogine,et al.  Exploring Complexity: An Introduction , 1989 .

[25]  Thomas L. Marzetta,et al.  Multiple-antenna channel hardening and its implications for rate feedback and scheduling , 2004, IEEE Transactions on Information Theory.

[26]  Pierre Priouret,et al.  Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.

[27]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[28]  S. Thomas Alexander,et al.  Adaptive Signal Processing , 1986, Texts and Monographs in Computer Science.

[29]  S. M. Sze,et al.  Scanning the issue - Special issue on nanoelectronics and nanoscale processing , 2003 .

[30]  S. Wind,et al.  Carbon nanotube electronics , 2003, Digest. International Electron Devices Meeting,.

[31]  Eric Moulines,et al.  Inference in hidden Markov models , 2010, Springer series in statistics.

[32]  D. Slepian,et al.  Prolate spheroidal wave functions, fourier analysis and uncertainty — II , 1961 .

[33]  Donald B. Percival,et al.  Spectral Analysis for Physical Applications , 1993 .

[34]  D. Thomson,et al.  Spectrum estimation and harmonic analysis , 1982, Proceedings of the IEEE.

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

[36]  T. J. Shepard.  Decentralized Channel Management in Scalable Multihop Spread-Spectrum Packet Radio Networks , 1995 .

[37]  J. M. Smith.  The theory of games and the evolution of animal conflicts. , 1974, Journal of theoretical biology.

[38]  Fumihito Arai,et al.  Assembly of nanodevices with carbon nanotubes through nanorobotic manipulations , 2003, Proc. IEEE.

[39]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[40]  Ekram Hossain,et al.  Radio Resource Management in Wireless Networks , .

[41]  Joseph Mitola,et al.  The software radio architecture , 1995, IEEE Commun. Mag..

[42]  Petre Stoica,et al.  On nonparametric spectral estimation , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[43]  Haikady N. Nagaraja,et al.  Inference in Hidden Markov Models , 2006, Technometrics.

[44]  L. J. Griffiths,et al.  An alternative approach to linearly constrained adaptive beamforming , 1982 .

[45]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[46]  E. Rowland.  Theory of Games and Economic Behavior , 1946, Nature.

[47]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[48]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[49]  Theodore S. Rappaport,et al.  Smart antennas: Adaptive arrays, algorithms, & wireless position location , 1998 .

[50]  Anthony Ralston,et al.  Encyclopedia of Computer Science , 1971 .

[51]  Eric van Damme,et al.  Non-Cooperative Games , 2000 .

[52]  Claude Berrou,et al.  The ten-year-old turbo codes are entering into service , 2003, IEEE Commun. Mag..

[53]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[54]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

[55]  S. Iijima.  Helical microtubules of graphitic carbon , 1991, Nature.

[56]  O. Hoshuyama,et al.  A robust adaptive beamformer for microphone arrays with a blocking matrix using constrained adaptive filters , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[57]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[58]  Simon Haykin,et al.  Uncovering nonlinear dynamics-the case study of sea clutter , 2002, Proc. IEEE.

[59]  David J. Thomson,et al.  Noise in wireless systems produced by solar radio bursts , 2001 .

[60]  Akihiko Sugiyama,et al.  A robust adaptive beamformer for microphone arrays with a blocking matrix using constrained adaptive filters , 1999, IEEE Trans. Signal Process..

[61]  P. Glimcher.  Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics , 2003 .

[62]  No-Regret Algorithms for Structured Prediction Problems , 2005 .

[63]  D. Slepian.  Prolate spheroidal wave functions, fourier analysis, and uncertainty — V: the discrete case , 1978, The Bell System Technical Journal.

[64]  Michael E. Mann,et al.  Oscillatory Spatiotemporal Signal Detection in Climate Studies: A Multiple-Taper Spectral Domain Approach , 1999 .