NDLI: Detection performance of multibranch and multichannel compressive receivers

Content Provider	IEEE Xplore Digital Library
Author	Pollock, B. Goodman, N.A.
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Computer Engineering, The University of Arizona, Tucson, 85721, USA (Pollock, B.) \|\| School of Electrical and Computer Engineering, The University of Oklahoma, Norman, 73019, USA (Goodman, N.A.)
Abstract	Some proposed architectures for compressive sampling of time-domain signals involve splitting the signal into multiple branches. This approach is not unlike the idea of splitting a signal into in-phase and quadrature branches for a coherent receiver implementation, but in the case of a compressive receiver the idea is to modulate the signal in each branch against a different measurement kernel. Therefore, multiple projections can be captured in parallel over the same time interval of the signal. In this paper, we evaluate the detection performance of multibranch compressive receiver implementations. The noise statistics over receiver branches are derived and shown to depend on the quality of the low-noise amplifier (LNA). A high-gain, low-noise-figure amplifier causes correlated noise across the receiver branches, but avoids SNR loss due to the power divider. We discuss whether a multibranch approach can provide equal or better detection performance than a single-branch compressive receiver, and include comparisons for varying LNA quality. A comparison to a true multichannel implementation is also provided.
Starting Page	341
Ending Page	344
File Size	414607
Page Count	4
File Format	PDF
ISBN	9781467310703
ISSN	2151870X
e-ISBN	9781467310710
DOI	10.1109/SAM.2012.6250506
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Receivers Power dividers Kernel Signal to noise ratio Noise figure
Content Type	Text
Resource Type	Article

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