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# Distributed Adaptive Norm Estimation For Blind System Identification in Wireless Sensor Networks | ||
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Distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i.e., fusion center) for processing. | ||
In this contribution, we extend a distributed adaptive algorithm for blind system identification that relies on the estimation of a stacked network-wide consensus vector at each node, the computation of which requires either broadcasting or relaying of node-specific values (i.e., local vector norms) to all other nodes. | ||
The extended algorithm employs a distributed-averaging-based scheme to estimate the network-wide consensus norm value by only using the local vector norm provided by neighboring sensor nodes. | ||
We introduce an adaptive mixing factor between instantaneous and recursive estimates of these norms for adaptivity in a time-varying system. | ||
Simulation results show that the extension provides estimation results close to the optimal fully-connected-network or broadcasting case while reducing inter-node transmission significantly. | ||
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## Repository content | ||
This repository contains all code used to generate the paper submission. | ||
- Python simulation code | ||
## Instructions | ||
In order to run the simulation, do the following: | ||
- clone repository: ```git clone https://github.com/SOUNDS-RESEARCH/icassp2023-adapt-dist-avg.git``` | ||
- init submodule: ```git submodule update --init --recursive``` | ||
- create virtual python environment [optional] | ||
- install dependencies: ```pip install -r requirements.txt``` | ||
- run simulations: ```python simulations/static.py``` and ```python simulations/dynamic.py``` | ||
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## SOUNDS | ||
This research work was carried out at the ESAT Laboratory of KU Leuven, in the frame of the SOUNDS European Training Network. | ||
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[SOUNDS Website](https://www.sounds-etn.eu/) | ||
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## Acknowledgements | ||
<table> | ||
<tr> | ||
<td width="75"> | ||
<img src="https://www.sounds-etn.eu/wp-content/uploads/2021/01/Screenshot-2021-01-07-at-16.50.22-600x400.png" align="left"/> | ||
</td> | ||
<td> | ||
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956369 | ||
</td> | ||
</tr> | ||
</table> |
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cvxpy==1.2.1 | ||
matplotlib==3.5.1 | ||
numpy==1.22.0 | ||
scipy==1.7.3 | ||
pandas==1.4.1 | ||
pickleshare==0.7.5 |