We consider linear regression under a model where the parameter vector is known to be sparse. Using a Bayesian framework, we derive a computationally efficient approximation to the minimum mean-square error (MMSE) estimate of the parameter vector. The performance of the so-obtained estimate is illustrated via numerical examples.
—This article considers spectrum-on-demand in a cellular system. A communication system that wants to access spectrum to which it does not own a license must perform spectrum sensing to identify spectrum opportunities, and to guarantee that it does not cause unacceptable interference to the license owner. Because a single sensor may be in a fading dip,… (More)
The use of phased-array receive coils is a well-known technique to improve the image quality in magnetic resonance imaging studies of, e.g., the human brain. It is common to incorporate proton (1H) magnetic resonance spectroscopy (MRS) experiments in these studies to quantify key metabolites in a region of interest. Detecting metabolites in vivo is often… (More)
—Cellular networks today are designed for and operate in dedicated licensed spectrum. At the same time there are other spectrum usage authorization models for wireless communication, such as unlicensed spectrum or, as widely discussed currently but not yet implemented in practice, various forms of licensed shared spectrum. Hence, cellular technology as of… (More)