Hybrid Message Passing Algorithm for Downlink FDD Massive MIMO-OFDM Channel Estimation

TL;DR

This paper introduces a hybrid message passing algorithm tailored for downlink FDD massive MIMO-OFDM channel estimation, leveraging a novel structured prior model to improve convergence and accuracy over existing methods.

Contribution

It develops a hybrid message passing rule for complex probability models and applies it within a structured turbo framework for enhanced channel estimation.

Findings

Faster convergence compared to existing algorithms

Improved stability and performance in high SNR regimes

Maximum 3 dB gain over benchmarks

Abstract

The design of message passing (MP) algorithms on factor graphs is an effective manner to implement channel estimation (CE) in wireless communication systems, which performance can be further improved by exploiting prior probability models that accurately match the channel characteristics. In this work, we study the CE problem in a downlink massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. As the prior probability, we propose the Markov chain two-state Gaussian mixture with large variance differences (TSGM-LVD) model to exploit the structured sparsity in the angle-frequency domain of the channel. Existing single and combined MP rules cannot deal with the message computation of the proposed probability model. To overcome this issue, we present a general method to derive the hybrid message passing (HMP) rule, which allows the calculation of messages described by mixed linear and non-linear functions. Accordingly, we design the HMP-TSGM-LVD algorithm under the structured turbo framework (STF). Simulation results demonstrate that the proposed algorithm converges faster and obtains better and more stable performance than its counterparts. In particular, the gain of the proposed approach is maximum (3 dB) in the high signal-to-noise ratio regime, while benchmark approaches experience oscillating behavior due to the improper prior model characterization.