NDLI: Distributed cognitive spectrum sensing via group sparse total least-squares

Content Provider	IEEE Xplore Digital Library
Author	Dall'Anese, E. Giannakis, G.B.
Copyright Year	2011
Description	Author affiliation: Dept. of Electrical and Computer Eng., University of Minnesota, Minneapolis, USA (Dall'Anese, E.; Giannakis, G.B.)
Abstract	Dynamic re-use of licensed bands under the hierarchical spectrum access paradigm calls for innovative network-level sensing algorithms for spectrum opportunity awareness in the frequency, time, and space dimensions. Toward this direction, the present paper develops a distributed spectrum sensing algorithm whereby cognitive radios (CRs) cooperate to localize active primary user (PU) transmitters, and estimate their transmit-power spectral densities. The sensing scheme relies on a parsimonious linear system model that accounts for two forms of sparsity: one due to the narrow-band nature of PU transmissions compared to the large swath of monitored frequencies; and another one emerging when employing a spatial grid of candidate PU locations. Capitalizing on this dual sparsity, and combining the merits of Lasso, group Lasso, and total least-squares (TLS), a group sparse (GS) TLS problem is formulated to obtain hierarchically-sparse model estimates, and cope with model uncertainty induced by channel randomness, and grid-induced model offsets. The GS-TLS problem is collaboratively solved by the CRs in a distributed fashion, using only local message exchanges among neighboring nodes. In spite of the non-convexity of the GS-TLS criterion, the novel distributed algorithm has guaranteed convergence to (at least) a locally optimal solution. The analytical findings are corroborated by numerical tests.
Starting Page	341
Ending Page	344
File Size	353522
Page Count	4
File Format	PDF
ISBN	9781457721045
e-ISBN	9781457721052
DOI	10.1109/CAMSAP.2011.6136021
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fading Zirconium Vectors Sensors Mathematical model Niobium Shadow mapping
Content Type	Text
Resource Type	Article

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