Dictionary Learning for Reconstructing Measurements of Analog Wireless Sensor Nodes

Authors: C. Willuweit, C. Bockelmann, A. Dekorsy
Abstract:

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.

Document type: Conference Paper
Publication: Rostock, Germany, 11. - 14. February 2019
Conference: 12th International ITG Conference on Systems, Communications and Coding (SCC)
Files:
conference_071817.pdf510 KB
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Last change on 26.03.2019 by C. Willuweit
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