Convolutional-Type Neural Networks for Fading Channel Forecasting

Lia Ahrens, Julian Ahrens, Hans Dieter Schotten

In: IEEE Access 8 Pages 193075-193090 IEEE 10/2020.


In this article, a series of convolutional-type predictive neural networks are proposed for the issue of fading channel forecasting for orthogonal frequency-division multiplexing (OFDM) transmission systems in a multiple-input and multiple-output (MIMO) mode via a noisy channel. The proposed neural networks all employ convolutional connections that operate in a translation-invariant manner in the frequency domain of the time-varying channel transfer function, which effectively tackles the essential challenges of high dimensionality and denoising. Each of the proposed convolutional-type neural networks is built on a specific overall network architecture and functions as an independent predictor that offers advantages regarding a specific aspect such as accuracy over a certain prediction span or computational effort. Comparative evaluations against common prediction methods such as the Kalman filtering scheme and the standard long-short term memory units (LSTMs) are provided on the basis of transmission simulations over dispersive fading channels with Rayleigh components according to the well-established 3GPP Long Term Evolution (LTE) standards.


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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz