Massive MIMO-OFDM Channel Acquisition with Time-Frequency Phase-Shifted Pilots

TL;DR

This paper introduces a novel channel acquisition method for massive MIMO-OFDM systems using time-frequency phase-shifted pilots, improving estimation accuracy and reducing interference with low complexity.

Contribution

It develops a triple-beam tensor model and a tensor-based information geometry approach for efficient channel estimation with limited pilot overhead.

Findings

Reduces NMSE by over 8 dB in large UT scenarios

Effectively suppresses inter-UT interference

Demonstrates superior performance through extensive simulations

Abstract

In this paper, we propose a channel acquisition approach with time-frequency phase-shifted pilots (TFPSPs) for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We first present a triple-beam (TB) based channel tensor model, allowing for the representation of the space-frequency-time (SFT) domain channel as the product of beam matrices and the TB domain channel tensor. By leveraging the specific characteristics of TB domain channels, we develop TFPSPs, where distinct pilot signals are simultaneously transmitted in the frequency and time domains. Then, we present the optimal TFPSP design and provide the corresponding pilot scheduling algorithm. Further, we propose a tensor-based information geometry approach (IGA) to estimate the TB domain channel tensors. Leveraging the specific structure of beam matrices and the properties of TFPSPs, we propose a low-complexity implementation of the tensor-based IGA. We validate the efficiency of our proposed channel acquisition approach through extensive simulations. Simulation results demonstrate the superior performance of our approach. The proposed approach can effectively suppress inter-UT interference with low complexity and limited pilot overhead, thereby enhancing channel estimation performance. Particularly in scenarios with a large number of UTs, the channel acquisition method outperforms existing approaches by reducing the normalized mean square error (NMSE) by more than 8 dB.