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

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 wo...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 23; no. 5; pp. 4596 - 4609
Main Authors: Song, Yi, Zhang, Chuanzong, Lu, Xinhua, Saggese, Fabio, Wang, Zhongyong
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computational modeling
Conditional probability
Downlink
Downlinking
FDD channel estimation
Linear functions
Markov chains
Markov processes
Massive MIMO
Message passing
MIMO communication
OFDM
Orthogonal Frequency Division Multiplexing
Probability density function
Signal to noise ratio
System effectiveness
TSGM-LVD channel model
Wireless communication
Wireless communication systems
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary: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.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2023.3320760