Dictionary Learning for Reconstructing Measurements of Analog Wireless Sensor Nodes

Konferenz: SCC 2019 - 12th International ITG Conference on Systems, Communications and Coding
11.02.2019 - 14.02.2019 in Rostock, Germany


Tagungsband: SCC 2019

Seiten: 6Sprache: EnglischTyp: PDF

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Willuweit, Christopher; Bockelmann, Carsten; Dekorsy, Armin (Department of Communications Engineering, University of Bremen, Bremen, Germany)

Wireless Sensor Nodes communicating measurements to a base station is one of the scenarios in the emerging field of Machine-Type-Communication. Those systems rely on low complexity of the nodes, due to cost and energy consumption. The main idea of this paper is to employ a low complexity analog modulation scheme in the node, and combine it with state of the art digital signal processing in the base station. Specifically, we focus on Amplitude Modulation in a point to point scenario facing noise and hardware offsets. We show that under certain assumptions this transmission can be described by a linear model. Subsequently we utilize payload (measurement) signal structure, namely sparsity, to estimate the payload signals as well as the hardware offsets using a dictionary learning algorithm. Numerical simulations show, that for realistic noise assumptions the algorithms are able to reconstruct payload signals and estimate hardware offsets.