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.

翻译：文献中，机器学习（ML）已被部署于基站（BS）和用户设备（UE），以提升下行信道状态信息（CSI）的精度。然而，因UE功耗等因素，ML在UE端的实现可能不可行。受此问题驱动，我们提出一种位于BS的CSI学习机制（CSILaBS），旨在避免UE端使用ML。为此，通过利用BS的信道预测器（CP），在UE端引入一种轻量级预测函数（PF）用于反馈评估。CSILaBS降低了空口反馈开销，提升了CSI质量，并降低了UE的计算成本。此外，在多用户环境下，我们提出了多种利用PF选择反馈的机制，以提升CSI精度。我们还探讨了多种基于ML的CP算法，例如受ML启发的统计算法NeuralProphet（NP）。进一步，受统计模型与ML联合使用的启发，我们提出了一种由循环神经网络和NP组成的新型混合框架，该框架相比单一模型具有更优的预测精度。通过诺基亚贝尔实验室记录的实证数据集对CSILaBS性能进行评估，结果表明，在UE端消除ML仍能保持预编码质量等性能增益。