Benchmarking End-to-end Learning of MIMO Physical-Layer Communication

End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO transceivers, without any a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and multi-user MIMO and show that the gains of ML-based communication in the former two cases can be to a large extent ascribed to implicitly learned geometric shaping and bit and power allocation, not to learning new spatial encoders. For MU-MIMO, we demonstrate the feasibility of a novel method with centralized learning and decentralized executing, outperforming conventional zero-forcing. For each scenario, we provide explicit descriptions as well as open-source implementations of the selected neural-network architectures.Comment: 6 pages, 8 figures, conference pape

A survey about deep learning for constellation design in communications

Proceeding of: 12th IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, (CSNDSP), 20-22, July 2020, (Online).The performance of communication systems dependson the choice of constellations, designed in an end-to-endmanner. In case of a mathematical intractability, either becauseof complexity or even lack of channel model only sub-optimal solutionscan be provided with an analytical approach. We presentend-to-end learning, a recent technique in communications tolearn optimal transmitter and receiver architectures based ondeep neural networks (DNNs) architectures. We discuss cases inwhich this technique has been used to design constellations inwhich channel model intractability repressed from a mathematicalanalysis.This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie ETN TeamUp5G, grant agreement No. 813391, and from the Spanish National Project TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE)

Gradient-free training of autoencoders for non-differentiable communication channels

Training of autoencoders using the back-propagation algorithm is challenging for non-differential channel models or in an experimental environment where gradients cannot be computed. In this paper, we study a gradient-free training method based on the cubature Kalman filter. To numerically validate the method, the autoencoder is employed to perform geometric constellation shaping on differentiable communication channels, showing the same performance as the back-propagation algorithm. Further investigation is done on a non-differentiable communication channel that includes: laser phase noise, additive white Gaussian noise and blind phase search-based phase noise compensation. Our results indicate that the autoencoder can be successfully optimized using the proposed training method to achieve better robustness to residual phase noise with respect to standard constellation schemes such as Quadrature Amplitude Modulation and Iterative Polar Modulation for the considered conditions