Spectrum Sensing in Wideband OFDM Cognitive Radios

In this paper, spectrum sensing of an orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) is addressed. The goal is to identify the portions of the spectrum that are unused by primary user systems and other CR systems, called existing user (EU) systems altogether, with the emphasis on conquering the challenge imposed by multipath fading channel. The sensing of EU systems consists of two steps. In the first step, the maximum likelihood (ML) estimates of the frequency bands of EU systems are calculated; in the second step, detection is performed at each suspected band to decide whether an EU system is truly in operation. The idea is that an EU system appears at a segment of continuous subcarriers. This fact can be exploited by employing measurements at a continual subcarriers and executing the sensing along the frequency domain. An autoregressive (AR) model is adopted to track the variation of the received EU signal strength along frequencies. It is shown by simulations that the proposed spectrum sensing algorithm is robust in a severe frequency-selective fading channel.

[1]  Wang,et al.  Wireless Communication Systems , 2011 .

[2]  Gene H. Golub,et al.  Matrix computations , 1983 .

[3]  M.P. Wylie-Green,et al.  Dynamic spectrum sensing by multiband OFDM radio for interference mitigation , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[4]  Friedrich Jondral,et al.  Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency , 2004, IEEE Communications Magazine.

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

[6]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[7]  Carlo S. Regazzoni,et al.  Spectrum sensing: A distributed approach for cognitive terminals , 2007, IEEE Journal on Selected Areas in Communications.

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

[9]  Kiran Challapali,et al.  Cognitive PHY and MAC layers for dynamic spectrum access and sharing of TV bands , 2006, TAPAS '06.

[10]  C. Myers,et al.  A level building dynamic time warping algorithm for connected word recognition , 1981 .

[11]  H. Tang,et al.  Some physical layer issues of wide-band cognitive radio systems , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[12]  Van Trees,et al.  Detection, Estimation, and Modulation Theory. Part 1 - Detection, Estimation, and Linear Modulation Theory. , 1968 .

[13]  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..

[14]  Louis L. Scharf,et al.  Matched subspace detectors , 1994, IEEE Trans. Signal Process..

[15]  T. Ferguson.  A Course in Large Sample Theory , 1996 .

[16]  Mukund Desai,et al.  Robust Gaussian and non-Gaussian matched subspace detection , 2003, IEEE Trans. Signal Process..

[17]  Anant Sahai,et al.  Cooperative Sensing among Cognitive Radios , 2006, 2006 IEEE International Conference on Communications.

[18]  H. Chernoff.  On the Distribution of the Likelihood Ratio , 1954 .

[19]  Guoqing Li,et al.  Cross Layer Interference Mitigation Using Spectrum Sensing , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[20]  H. Vincent Poor,et al.  Spectrum Sensing in Cognitive Radios Based on Multiple Cyclic Frequencies , 2007, 2007 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[21]  Friedrich Jondral,et al.  Air interface recognition for a software radio system exploiting cyclostationarity , 2004, 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754).

[22]  National Chung-Hsing,et al.  BAYESIAN ESTIMATION OF THE NUMBER OF CHANGE POINTS , 2003 .

[23]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[24]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[25]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[26]  Ning Han,et al.  Spectral correlation based signal detection method for spectrum sensing in IEEE 802.22 WRAN systems , 2006, 2006 8th International Conference Advanced Communication Technology.

[27]  Geoffrey Ye Li,et al.  Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks , 2007, IEEE Transactions on Wireless Communications.

[28]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[29]  Lei Shi,et al.  Cognitive Radio: Methods for the Detection of Free Bands , 2009, 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing.

[30]  E. Visotsky,et al.  On collaborative detection of TV transmissions in support of dynamic spectrum sharing , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[31]  A. Ghasemi,et al.  Collaborative spectrum sensing for opportunistic access in fading environments , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[32]  Geoffrey Ye Li,et al.  Agility improvement through cooperative diversity in cognitive radio , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

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

[34]  Torbjørn Svendsen,et al.  On the automatic segmentation of speech signals , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[35]  Amir Ghasemi,et al.  Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs , 2008, IEEE Communications Magazine.

[36]  H. Vincent Poor,et al.  Wireless Communication Systems: Advanced Techniques for Signal Reception , 2003 .

[37]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[38]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[39]  Thomas Kailath,et al.  Detection of signals by information theoretic criteria , 1985, IEEE Trans. Acoust. Speech Signal Process..

[40]  Vladimir I. Kostylev,et al.  Energy detection of a signal with random amplitude , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[41]  J. I. Mararm,et al.  Energy Detection of Unknown Deterministic Signals , 2022 .

[42]  Danijela Cabric,et al.  Experimental study of spectrum sensing based on energy detection and network cooperation , 2006, TAPAS '06.

[43]  Robert W. Brodersen,et al.  Detect and avoid: an ultra-wideband/WiMAX coexistence mechanism [Topics in Radio Communications] , 2007, IEEE Communications Magazine.

[44]  Kang G. Shin,et al.  Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks , 2008, IEEE Transactions on Mobile Computing.

[45]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[46]  Geoffrey Ye Li,et al.  Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks , 2007, IEEE Transactions on Wireless Communications.

[47]  Friedrich Jondral,et al.  On the extraction of the channel allocation information in spectrum pooling systems , 2007, IEEE Journal on Selected Areas in Communications.

[48]  T. A. Weiss,et al.  A diversity approach for the detection of idle spectral resources in spectrum pooling systems , 2003 .