Spectral efficiency of spectrum-pooling systems

The authors investigate the idea of using cognitive radio to reuse locally unused spectrum to increase the total system capacity. The authors consider a multiband/wideband system in which the primary and cognitive users wish to communicate to different receivers, subject to mutual interference and assume that each user knows only his/her channel and the unused spectrum through adequate sensing. The basic idea under the proposed scheme is based on the notion of spectrum pooling. The idea is quite simple; a cognitive radio will listen to the channel and, if sensed idle, will transmit during the voids. It turns out that, although its simplicity, the proposed scheme showed very interesting features with respect to the spectral efficiency and the maximum number of possible pairwise cognitive communications. We impose the constraint that users successively transmit over available bands through selfish water filling. For the first time, our study has quantified the asymptotic (with respect to the band) achievable gain of using spectrum pooling in terms of spectral efficiency compared with classical radio systems. The authors then derive the total spectral efficiency as well as the maximum number of possible pairwise communications of such a spectrum-pooling system.

[1]  Sergio Verdú,et al.  Spectral efficiency in the wideband regime , 2002, IEEE Trans. Inf. Theory.

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

[3]  James S. Harris,et al.  Tables of integrals , 1998 .

[4]  K. V. Cai,et al.  Energy detector performance in a noise fluctuating channel , 1989, IEEE Military Communications Conference, 'Bridging the Gap. Interoperability, Survivability, Security'.

[5]  M. Melamed Detection , 2021, SETI: Astronomy as a Contact Sport.

[6]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .

[7]  Shlomo Shamai,et al.  Fading Channels: Information-Theoretic and Communication Aspects , 1998, IEEE Trans. Inf. Theory.

[8]  J. Crank Tables of Integrals , 1962 .

[9]  H. V. Trees Detection, Estimation, And Modulation Theory , 2001 .

[10]  Shlomo Shamai,et al.  Information theoretic considerations for cellular mobile radio , 1994 .

[11]  Mérouane Debbah,et al.  Distributed power allocation for cognitive radio , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[12]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise , 1992 .

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

[14]  Seyed Ali Ghorashi,et al.  Opportunistic scheduling using cognitive radio , 2006 .

[15]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[16]  D. F. Hays,et al.  Table of Integrals, Series, and Products , 1966 .

[17]  Mérouane Debbah,et al.  Spectral Efficiency of Cognitive Radio Systems , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[18]  Joseph Mitola Cognitive Radio for Flexible Mobile Multimedia Communications , 2001, Mob. Networks Appl..

[19]  Friedrich Jondral,et al.  Calculation of detection and false alarm probabilities in spectrum pooling systems , 2005, IEEE Communications Letters.

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

[21]  Friedrich K. Jondral,et al.  Efficient Signaling of Spectral Resources in Spectrum Pooling Systems , 2003 .

[22]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .