Convolutional-Type Neural Networks for Fading Channel Forecasting

Abstract

In this paper, 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. INDEX TERMS Wireless communications, deep learning, convolutional neural networks, LSTMs, time series analysis, fading channel forecasting, MIMO.

Hybrid Neural Network-Based Fading Channel Prediction for Link Adaptation

“…Lv et al [27] proposed a channel prediction scheme for a millimeter wave (mmWave) MIMO-OFDM system that considers the four following domains: array-frequency, array-time, angle-frequency, and angle-time. Ahrens et al [28] insisted that a shift-invariant prediction model could be applied to all subcarriers for channel prediction in the frequency domain because the utilized bandwidth is always significantly smaller than the carrier frequency. However, their assumption may not hold depending on the environment.…”