Data-Driven Estimation of Capacity Upper Bounds
Artikel i vetenskaplig tidskrift, 2022
We consider the problem of estimating an upper bound on the capacity of a memoryless channel with unknown channel law and continuous output alphabet. A novel data-driven algorithm is proposed that exploits the dual representation of capacity where the maximization over the input distribution is replaced with a minimization over a reference distribution on the channel output. To efficiently compute the required divergence maximization between the conditional channel and the reference distribution, we use a modified mutual information neural estimator that takes the channel input as an additional parameter. We numerically evaluate our approach on different memoryless channels and show empirically that the estimated upper bounds closely converge either to the channel capacity or to best-known lower bounds.
Artificial neural networks
channel capacity
upper capacity bounds
Channel estimation
mutual information neural estimation
Training
duality
Autoencoders
divergence estimation
Estimation
dual capacity representation
neural networks
Upper bound
Neurons
Mutual information
Författare
Christian Häger
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
Erik Agrell
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
IEEE Communications Letters
1089-7798 (ISSN) 15582558 (eISSN)
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Frigöra full fiberoptisk kapacitet
Knut och Alice Wallenbergs Stiftelse (KAW 2018.0090), 2019-07-01 -- 2024-06-30.
Styrkeområden
Informations- och kommunikationsteknik
Ämneskategorier
Telekommunikation
Kommunikationssystem
Signalbehandling
Infrastruktur
C3SE (Chalmers Centre for Computational Science and Engineering)
DOI
10.1109/LCOMM.2022.3207385