Nonlinear Compressive Sensing for Distorted Measurements and Application to Improving Efficiency of Power Amplifiers
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CitationChen, Hsieh-Chung, H.T. Kung, and Marcus Comiter. 2017. Nonlinear Compressive Sensing for Distorted Measurements and Application to Improving Efficiency of Power Amplifier. In 2017 IEEE International Conference on Communications (ICC), 1-7. Paris: IEEE.
AbstractCompressive sensing, which enables signal recovery from fewer samples than traditional sampling theory dictates, assumes that the sampling process is linear. However, this linearity assumption may not hold in the analog domain without significant trade-offs, such as power amplifiers sacrificing substantial power efficiency in exchange for producing linear outputs. Since compressive sensing is most impactful when implemented in the analog domain, it is of interest to integrate the nonlinearity in compressive measurements into the signal recovery process such that nonlinear effects can be mitigated. As such, in this paper, we describe a nonlinear compressive sensing formulation and associated signal recovery algorithms, providing both compression and improved efficiency of a power amplifier simultaneously with one procedure. We present evaluations of the proposed framework using both measurements from real power amplifiers and simulations.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:40996254
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