A Novel Algorithm to Report CSI in MIMO-Based Wireless Networks

TL;DR

This paper introduces a scalable CSI reporting method using channel prediction, significantly reducing feedback overhead and improving accuracy in both mobile and immobile wireless channels.

Contribution

It proposes a novel CSI reporting algorithm employing channel prediction at both ends, applicable to TDD and FDD systems, enhancing accuracy and reducing overhead.

Findings

Reduces channel mean-squared-error (MSE) in simulations.

Decreases over-the-air feedback overhead significantly.

Achieves SNR gains in mobile and immobile scenarios.

Abstract

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 In practice, due to processing and feedback delays, estimated CSI can be outdated, which can severely deteriorate the performance of the communication system.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.