  In the literature, machine learning (ML) has been implemented at the base
station (BS) and user equipment (UE) to improve the precision of downlink
channel state information (CSI). However, ML implementation at the UE can be
infeasible for various reasons, such as UE power consumption. Motivated by this
issue, we propose a CSI learning mechanism at BS, called CSILaBS, to avoid ML
at UE. To this end, by exploiting channel predictor (CP) at BS, a light-weight
predictor function (PF) is considered for feedback evaluation at the UE.
CSILaBS reduces over-the-air feedback overhead, improves CSI quality, and
lowers the computation cost of UE. Besides, in a multiuser environment, we
propose various mechanisms to select the feedback by exploiting PF while aiming
to improve CSI accuracy. We also address various ML-based CPs, such as
NeuralProphet (NP), an ML-inspired statistical algorithm. Furthermore, inspired
to use a statistical model and ML together, we propose a novel hybrid framework
composed of a recurrent neural network and NP, which yields better prediction
accuracy than individual models. The performance of CSILaBS is evaluated
through an empirical dataset recorded at Nokia Bell-Labs. The outcomes show
that ML elimination at UE can retain performance gains, for example, precoding
quality.
