A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO With Mobility

Abstract

This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-timedomain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance. Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users' velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms.

Enhanced AI-Based CSI Prediction Solutions for Massive MIMO in 5G and 6G Systems

“…For instance, the BS or the UE may utilize the previous channel observations to predict the future CSI, thus reducing the impact of processing and feedback delays. CSI prediction is one of the classical problems in wireless communication, and analytical techniques such as autoregression [4], [5], polynomial extrapolation [6], prediction based on high-resolution parameters estimation [7]- [9], and others have been suggested [10]. However, such analytical models inherently rely on channel models that may not reflect the complexity of the channel evolution.…”

Manifold Learning-Based CSI Feedback in Massive MIMO Systems

“…In this paper, we revisit the problem of limited CSI feedback in FDD massive MIMO systems by means of manifold learning (ML) [20]- [22]. The previous research mainly focused on the characteristics of the channel structure in the angulardelay/frequency domain [16] [23] or the structure of the channel covariance matrix [24]- [26]. The intrinsic manifold structure where the CSI samples reside is neglected.…”