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Issue No.08 - Aug. (2012 vol.11)

pp: 1304-1316

Mingyan Liu , University of Michigan, Ann Arbor

Quanquan Liang , University of Michigan, Ann Arbor

Dongfeng Yuan , Shandong University, Jinan

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2011.150

ABSTRACT

The knowledge of channel statistics can be very helpful in making sound opportunistic spectrum access decisions. It is therefore desirable to be able to efficiently and accurately estimate channel statistics. In this paper, we study the problem of optimally placing sensing/sampling times over a time window so as to get the best estimate of the parameters of an on-off renewal channel. We are particularly interested in a sparse sensing regime with a small number of samples relative to the time window size. Using Fisher information as a measure, we analytically derive the best and worst sensing sequences under a sparsity condition. We also present a way to derive the best/worst sequences without this condition using a dynamic programming approach. In both cases the worst turns out to be the uniform sensing sequence, where sensing times are evenly spaced within the window. Interestingly the best sequence is also uniform but with a much smaller sensing interval that requires a priori knowledge of the channel parameters. With these results we argue that without a priori knowledge, a robust sensing strategy should be a randomized strategy. We then compare different random schemes using a family of distributions generated by the circular \beta ensemble, and propose an adaptive sensing scheme to effectively track time-varying channel parameters. We further discuss the applicability of compressive sensing in the context of this problem.

INDEX TERMS

Wireless sensor networks, Channel estimation, Maximum likelihood estimation, Radio spectrum management, uniform sensing., Spectrum sensing, channel estimation, Fisher information, random sensing, sparse sensing

CITATION

Mingyan Liu, Quanquan Liang, Dongfeng Yuan, "Channel Estimation for Opportunistic Spectrum Access: Uniform and Random Sensing",

*IEEE Transactions on Mobile Computing*, vol.11, no. 8, pp. 1304-1316, Aug. 2012, doi:10.1109/TMC.2011.150REFERENCES