Channel Estimation for mmWave MIMO-OFDM Systems in High-Mobility Scenarios: Instantaneous Model or Statistical Model?

TL;DR

This paper compares instantaneous and statistical channel models for mmWave MIMO-OFDM systems in high-mobility scenarios, proposing a tensor-based estimation method that favors the instantaneous model for better accuracy.

Contribution

It introduces a low-rank tensor decomposition approach for channel estimation in mmWave MIMO-OFDM systems and evaluates the effectiveness of instantaneous versus statistical models.

Findings

Instantaneous model-based estimation outperforms statistical models.

Tensor CP decomposition effectively captures mmWave channel characteristics.

Simulation results confirm the superiority of the instantaneous model in high-mobility environments.

Abstract

Classical linear statistical models, like the first-order auto-regressive (AR) model, are commonly used as channel model in high-mobility scenarios. However, compared to sub-6G, the effect of Doppler frequency shifts is more significant at millimeter wave (mmWave) frequencies, and the effectiveness of the statistical channel model in high-mobility mmWave scenarios should be reconsidered. In this paper, we investigate the channel estimation for mmWave multiple-input multiple-output-(MIMO) orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, with the focus on the comparison between the instantaneous channel model and the statistical channel model. For the instantaneous model, by leveraging the low-rank nature of mmWave channels and the multidimensional characteristics of MIMO-OFDM signals across space, time, and frequency, the received signals are structured as a fourth-order tensor fitting a low-rank CANDECOMP/PARAFAC (CP) model. Then, to solve the CP decomposition problem, an estimation of signal parameters via rotational invariance techniques (ESPRIT)-type decomposition based channel estimation method is proposed by exploring the Vandermonde structure of factor matrix, and the channel parameters are then estimated from the factor matrices. We analyze the uniqueness condition of the CP decomposition and develop a concise derivation of the Cramer-Rao bound (CRB) for channel parameters. Simulations show that our method outperforms the existing benchmarks. Furthermore, the results based on the wireless environment generated by Wireless InSite verify that the channel estimation based on the instantaneous channel model performs better than that based on the statistical channel model. Therefore, the instantaneous channel model is recommended for designing channel estimation algorithm for mmWave systems in high-mobility scenarios.