Tech Briefs

A paper discusses recent developments in the area of Compressive Sensing (CS) for data loss in wireless sensing applications. Since many physical signals of interest are known to be sparse or compressible, employing CS not only compresses the data and reduces the effective transmission rate, but also improves the robustness of the system to channel erasures. This is possible because reconstruction algorithms for compressively sampled signals are not hampered by the stochastic nature of wireless link disturbances, which has traditionally plagued attempts at proactively handling the effects of these errors.

This paper proposes that if CS is employed for source compression, then CS can further be exploited as an application layer erasure coding strategy for recovering missing data. The paper shows that CS erasure encoding (CSEC) with random sampling is efficient for handling missing data in erasure channels, paralleling the performance of BCH codes, with the added benefit of graceful degradation of the reconstruction error even when the amount of missing data far exceeds the designed redundancy. Further, since CSEC is equivalent to nominal oversampling in the incoherent measurement basis, it is computationally less expensive than conventional erasure coding.

This work was done by Zainul Charbiwala, Supriyo Chakraborty, Sadaf Zahedi, Younghun Kim, and Mani B. Srivastava of the University of California, Los Angeles; and Tin He and Chatschik Bisdikian of IBM T.J. Watson Research Center for the Army Research Laboratory. For more information, download the Technical Support Package (free white paper) at under the Physical Sciences category. ARL-0097

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Compressive Oversampling for Robust Data Transmission in Sensor Networks (reference ARL-0097) is currently available for download from the TSP library.

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