Message-Passing Receiver for Joint Channel Estimation and Decoding in 3D Massive MIMO-OFDM Systems

TL;DR

This paper introduces a computationally efficient message-passing receiver for 3D massive MIMO-OFDM systems that jointly estimates channels and decodes signals using expectation propagation and EM-based learning, outperforming existing methods.

Contribution

The paper presents a novel joint channel estimation and decoding framework using expectation propagation with second-order Taylor expansion and Wirtinger calculus, tailored for 3D massive MIMO-OFDM systems.

Findings

The proposed receiver achieves superior performance compared to traditional methods.

The algorithm effectively estimates channel impulse responses with reduced complexity.

Empirical results validate the approach's robustness in spatially correlated channels.

Abstract

In this paper, we address the message-passing receiver design for the 3D massive MIMO-OFDM systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. Specially, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response (CIR) between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization (EM)-based method is derived to learn the channel power-delay-profile (PDP). The proposed joint algorithm is assessed in 3D massive MIMO systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and complexity.