  In wireless communication, accurate channel state information (CSI) is of
pivotal importance. In practice, due to processing and feedback delays,
estimated CSI can be outdated, which can severely deteriorate the performance
of the communication system. Besides, to feedback estimated CSI, a strong
compression of the CSI, evaluated at the user equipment (UE), is performed to
reduce the over-the-air (OTA) overhead. Such compression strongly reduces the
precision of the estimated CSI, which ultimately impacts the performance of
multiple-input multiple-output (MIMO) precoding. Motivated by such issues, we
present a novel scalable idea of reporting CSI in wireless networks, which is
applicable to both time-division duplex (TDD) and frequency-division duplex
(FDD) systems. In particular, the novel approach introduces the use of a
channel predictor function, e.g., Kalman filter (KF), at both ends of the
communication system to predict CSI. Simulation-based results demonstrate that
the novel approach reduces not only the channel mean-squared-error (MSE) but
also the OTA overhead to feedback the estimated CSI when there is immense
variation in the mobile radio channel. Besides, in the immobile radio channel,
feedback can be eliminated, which brings the benefit of further reducing the
OTA overhead. Additionally, the proposed method provides a significant
signal-to-noise ratio (SNR) gain in both the channel conditions, i.e., highly
mobile and immobile.
