In this paper, we propose a distributed cross-relation-based adaptive algorithm for blind identification of single-input multiple-output (SIMO) systems in the frequency domain, using the alternating direction method of multipliers (ADMM) in a wireless sensor network (WSN). The network consists of a fixed number of nodes each equipped with a processing unit and a sensor that represents an output channel of the SIMO system. The proposed algorithm exploits the separability of the cross-channel relations by splitting the multichannel identification problem into sub-problems containing a subset of channels, in a way that is determined by the network topology. Each node delivers estimates for the subset of channel frequency responses, which are then combined into a consensus estimate per channel using general-form consensus ADMM in an adaptive updating scheme. Using numerical simulations, we show that it is possible to achieve convergence speeds and steady-state misalignment values comparable to fully centralized low-cost frequency-domain algorithms.
This repository contains all code used to generate the paper submission.
- Python simulation code and plot generation code
- algorithms: centralized and distributed algorithms used in simulations
- simulation_1: Random impulse responses, M=5, ring topology
- simulation_2: Random impulse reponses, M=4, M=8, random topology
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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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 |