Spectrum sensing and channel estimation are two important examples of background tasks needed for efficient wireless network operations. Channel and spectrum state communication overheads can become a serious burden, unless appropriate sensing and estimation strategies are designed that can do the job well with very limited, judicious feedback. This thesis considers two `frugal' sensing and estimation problems in this regime: crowdsourced power spectrum sensing using a network of low-end sensors broadcasting few bits; and channel estimation and tracking for transmit beamforming in frequency-division duplex (FDD) mode.In the case of spectrum sensing, each sensor is assumed to pass the received signal through a random wideband filter, measure the average power at the output of the filter, and send out a single bit to a fusion center (FC) depending on its measurement. Exploiting linearity with respect to the autocorrelation as well as important non negativity properties in a novel linear programming (LP) formulation, it is shown that adequate power spectrum sensing is possible from few bits, even for dense spectra. The formulation can be viewed as generalizing classical nonparametric spectrum estimation to the case where the data is in the form of inequalities, rather than equalities. Taking into account fading and insufficient sample averaging considerations, a different convex maximum likelihood (ML) formulation is developed, outperforming the LP formulation when the power estimates prior to thresholding are noisy. Assuming availability of a downlink channel that the FC can use to send threshold information, active sensing strategies are developed which quickly narrow down the power spectrum estimate.For the downlink channel tracking problem, the receiver is assumed to send back to the transmitter a coarsely quantized version of the received transmitter-beamformed pilot signal, instead of sending quantized channel information as in codebook-based beamforming. A novel channel tracking approach is proposed that exploits the quantization bits in a maximum a posteriori (MAP) estimation formulation, and closed-form expressions for the channel estimation mean-squared error and the corresponding signal-to-noise ratio are derived under certain conditions.
University of Minnesota Ph.D. dissertation. April 2014. Majoe: Electrical Engineering. Advisors: Professor Nicholas D. Sidiropoulos, Advisor
Professor Georgios B. Giannakis, Co-Advisor. 1 coputer file (PDF); viii, 110 pages, appendices A-B.
Frugal sensing and estimation over wireless networks.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.