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In this paper, we consider the problem of exact support recovery of sparse signals via noisy linear measurements. The main focus is finding the sufficient and necessary condition on the number of measurements for support recovery to be reliable. By drawing an analogy between the problem of support recovery and the problem of channel coding over the Gaussian(More)
In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly model the measurement noise as a combination of two terms; the first term accounts for regular measurement noise modeled as zero mean Gaussian noise, and the second term captures(More)
Due to poor signal strength, multipath effects, and limited on-device computation power, common GPS receivers do not work indoors. This work addresses these challenges by using a steerable, high-gain directional antenna as the front-end of a GPS receiver along with a robust signal processing step and a novel location estimation technique to achieve direct(More)
This paper studies the performance limits in the support recovery of sparse signals based on multiple measurement vectors (MMV). An information-theoretic analytical framework inspired by the connection to the single-input multiple-output multiple-access channel communication is established to reveal the performance limits in the support recovery of sparse(More)
Web search is seeing a paradigm shift from keyword based search to an entity-centric organization of web data. To support web search with this deeper level of understanding, a web-scale entity linking system must have 3 key properties: First, its feature extraction must be robust to the diversity of web documents and their varied writing styles and content(More)
In this paper, we examine the performance limits of the Orthogonal Matching Pursuit (OMP) algorithm, which has proven to be effective in solving for sparse solutions to inverse problem arising in overcomplete representations. To identify these limits, we exploit the connection between sparse solution problem and multiple access channel (MAC) in wireless(More)
Location is a fundamental service for mobile computing. Typical GPS receivers, although widely available for navigation purposes, may consume too much energy to be useful for many applications. Observing that in many sensing scenarios, the location information can be post-processed when the data is uploaded to a server, we design a cloud-offloaded GPS(More)
In this paper, we examine the problem of overcomplete representations and provide new insights into the problem of stable recovery of sparse solutions in noisy environments. We establish an important connection between the inverse problem that arises in overcomplete representations and wireless communication models in network information theory. We show(More)
Following rising demands in positioning with GPS, low-cost receivers are becoming widely available; but their energy demands are still too high. For energy efficient GPS sensing in delay-tolerant applications, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a(More)
We present a robust approach to modeling voiced speech using a family of minimum variance distortionless response (MVDR) spectral estimates. The method exploits the fact that for a fixed model order, for a sinusoidal signal in noise, the MVDR estimate at the sinusoidal frequency is approximately related to the sinusoidal and noise power in a simple linear(More)