Fading Channel Prediction for 5G and 6G Mobile Communication Systems
Abstract
Nowadays, there is a trend to employ adaptive so-
lutions in mobile communication. The adaptive transmission sys-
tems seem to answer the need for highly reliable communication
that serves high data rates. For efficient adaptive transmission,
the future Channel State Information (CSI) has to be known. The
various prediction methods can be applied to estimate the future
CSI. However, each method has its bottlenecks. The task is even
more challenging while considering the future 5G/6G communi-
cation where the employment of sub-6 GHz and millimetre waves
(mmWaves) in narrow-band, wide-band and ultra-wide-band
transmission is considered. Thus, we describe the differences
between sub-6 GHz/mmWave and narrow-band/wide-band/ultra-
wide-band channel prediction, provide a comprehensive overview
of available prediction methods, discuss its performance and
analyse the opportunity to use them in sub-6 GHz and mmWave
systems. We select Long Short-Term Memory Recurrent Neural
Network (RNN) as the most promising technique for future CSI
prediction and propose optimising one of its parameters - the
number of input features, which was not yet considered as an
opportunity to improve the performance of CSI prediction.
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