| ABSTRACT
Spectrum sensing is critical for cognitive systems to locate
spectrum holes. In the IEEE 802.22 proposal, short quiet periods are
arranged inside frames to perform a coarse intra-frame sensing as a
pre-alarm for fine inter-frame sensing. However, the limited sample
size of the quiet periods may not guarantee a satisfying performance
and an additional burden of quiet-period synchronization is
required. To improve the sensing performance, we first propose a
quiet-active sensing scheme in which inactive customer-provided
equipments (CPEs) will sense the channels in both the quiet and
active periods. To avoid quiet-period synchronization, we further
propose to utilize (optimized) active sensing, in which the quiet
periods are replaced by `quiet samples' in other domains, such as
quiet sub-carriers in OFDMA systems. By doing so, we not only save
the need for synchronization, but also achieve selection diversity
by choosing quiet sub-carriers based on channel conditions. The
proposed active sensing scheme is also promising for spectrum
sharing applications where both the cognitive and primary systems can be active
simultaneously.
References
- D. Cavalcanti and M. Ghosh, ``Cognitive radio networks: enabling new
wireless broadband opportunities,'' in Proc. 3rd International Conference
on Cognitive Radio Oriented Wireless Networks and Communications,
pp. 1-6, May 2008.
- C. Cordeiro, M. Ghosh, D. Cavalcanti, and K. Challapali, ``Spectrum
sensing for dynamic spectrum access of TV bands,''
in Proc. 2nd International Conference on Cognitive Radio Oriented
Wireless Networks and Communications, pp. 225-233, Aug. 2007.
- Y. H. Zeng, C. L. Koh, and Y.-C. Liang, ``Maximum eigenvalue detection:
theory and application,'' IEEE International Conference on Communications, 2008.
- Y. H. Zeng and Y.-C. Liang, ``Maximum-minimum eigenvalue detection
for congitive radio,'' IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 2007.
- Z. Quan, S. Cui, and A. H. Sayed, ``Optimal linear cooperation for
spectrum sensing in cognitive radio networks," IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 28-40, Feb. 2008.
- C. Sun, W. Zhang, and K. B. Letaief, ``Cooperative spectrum sensing
for cognitive radios under bandwidth constraints,'' in Proc. IEEE WCNC, Hong Kong, Mar. 2007, pp. 1-5.
- Y.-C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang,
``Sensing-throughput tradeoff for cognitive radio networks,'' IEEE Trans. Wireless Commun., vol. 7, pp. 1326-1337, Apr. 2008.
- N. Hoven and A. Sahai, ``Power scaling for cognitive radio,'' in
Proc. WCNC, Maui, Hawaii, USA, June 2005, vol. 1, pp. 250-255.
- K. Hamdi, W. Zhang, and K. B. Letaief, ``Power control in cognitive
radio systems based on spectrum sensing side information,'' in Proc. IEEE International Conference on Communications, June 2007, pp. 5161-5165.
- S. H. Song and Q. T. Zhang, ``Mutual information of wireless systems with
imperfect channel information,'' IEEE Trans. Commun., vol. 57, no. 2, pp. 1523-1531, May 2009.
- G. Ganesan, Y. Li, B. Bing, and S. Li, ``Spatiotemporal sensing in
cognitive radio networks,'' IEEE J. Sel. Areas Commun.. vol. 26, no. 1, pp. 5-12, Jan. 2008.
- Y. Zeng, Y. C. Liang, A. T. Hoang, and R. Zhang, ``A review on spectrum
sensing techniques for cognitive radio: challenges and solutions,'' in press, Eurasip J. Advances in Signal Process.
- EN 300 744 - V1.5.1 - Digital Video Broadcasting (DVB); framing
structure, channel coding and modulation for digital terrestrial television, http://www.etsi.org.
- L. L. Scharf, Statistical Signal Processing: Detection,
Estimation and Time Series Analysis. Addison-Wesley Publishing Comparny, Inc., 1991.
- J. P. Imhof, ``Computing the distribution of quadratic forms
in normal variables,'' Biometrika, vol. 48, nos. 3-4, pp. 419-426, 1961.
- S. H. Song and Q. T. Zhang, ``Multi-dimensional detector for UWB
ranging system in dense multi-path environment,''
IEEE Trans. Wireless Commun., vol. 7, no. 1, pp. 175-183, Jan. 2008.
|