author = {C. Willuweit and C. Bockelmann and A. Dekorsy},
  year = {2019},
  month = {Feb},
  title = {Dictionary Learning for Reconstructing Measurements of Analog Wireless Sensor Nodes},
  URL = {https://www.scc2019.net/},
  keywords = {machine type communication, wireless sensor networks, analog sensor communication, amplitude modulation, hardware offsets, dictionary learning, K-SVD},
  address={Rostock, Germany},
  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.},
  booktitle={12th International ITG Conference on Systems, Communications and Coding (SCC)}