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A spatially distributed network contains a large amount of agents with limited sensing, data processing, and communication capabilities. Recent technological advances have opened up possibilities to deploy spatially distributed networks for signal sampling and reconstruction. In this paper, we introduce a graph structure for a distributed sampling and reconstruction system by coupling agents in a spatially distributed network with innovative positions of signals. A fundamental problem in sampling theory is the robustness of signal reconstruction in the presence of sampling noises. For a distributed sampling and reconstruction system, the robustness could be reduced to the stability of its sensing matrix. In this paper, we split a distributed sampling and reconstruction system into a family of overlapping smaller subsystems, and we show that the stability of the sensing matrix holds if and only if its quasi-restrictions to those subsystems have uniform stability. This new stability criterion could be pivotal for the design of a robust distributed sampling and reconstruction system against supplement, replacement and impairment of agents, as we only need to check the uniform stability of affected subsystems. In this paper, we also propose an exponentially convergent distributed algorithm for signal reconstruction, that provides a suboptimal approximation to the original signal in the presence of bounded sampling noises.
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VAE for Joint Source-Channel Coding of Distributed Gaussian Sources over AWGN MAC
In this paper, we introduce a framework for Joint Source-Channel Coding of distributed Gaussian sources over a multiple access AWGN channel. Although there are prior works that have studied this, they either strongly rely on intuition to design encoders and decoder or require the knowledge of the complete joint distribution of all the distributed sources. Our system overcomes this. We model our system as a Variational Autoencoder and leverage insight provided by this connection to propose a crucial regularization mechanism for learning. This allows us to beat the state of the art by improving the signal reconstruction quality by almost 1dB for certain configurations. The end-to-end learned system is also found to be robust to channel condition variations of ±5dB and shows a drop in signal reconstruction quality by at most 1dB. Finally, we propose a novel lower bound on the optimal distortion in signal reconstruction and empirically showcase the tightness of the bound in comparison with the existing bound.
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- Award ID(s):
- 2003002
- PAR ID:
- 10293689
- Date Published:
- Journal Name:
- IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SPAWC48557.2020.9154331
