Design and Analysis of Downlink Channel Estimation Based on Parametric Model for Massive MIMO in FDD Systems

TL;DR

This paper proposes a parametric model-based downlink channel estimation method for FDD massive MIMO systems with cascaded precoding, enabling near-MMSE performance by utilizing uplink path delay estimates.

Contribution

It introduces a novel downlink channel estimation strategy based on uplink path delay estimation and quantization, improving estimation accuracy in massive MIMO FDD systems.

Findings

Achieves near-MMSE channel estimation performance.

Reduces downlink training and feedback overhead.

Utilizes uplink path delay information for downlink estimation.

Abstract

This paper investigates downlink channel estimation in frequency-division duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems. To reduce the overhead of downlink channel estimation and uplink feedback in FDD systems, cascaded precoding has been used in massive MIMO such that only a low-dimensional effective channel needs to be estimated and fed back. On the other hand, traditional channel estimations can hardly achieve the minimum mean-square-error (MMSE) performance due to lack of the a priori knowledge of the channels. In this paper, we design and analyze a strategy for downlink channel estimation based on the parametric model in massive MIMO with cascaded precoding. For a parametric model, channel frequency responses are expressed using the path delays and the associated complex amplitudes. The path delays of uplink channels are first estimated and quantized at the base station, then fed forward to the user equipment (UE) through a dedicated feedforward link. In this manner, the UE can obtain the a priori knowledge of the downlink channel in advance since it has been demonstrated that the downlink and the uplink channels can have identical path delays. Our analysis and simulation results show that the proposed approach can achieve near-MMSE performance.