Joint Channel Estimation and Signal Detection for MIMO-OFDM: A Novel Data-Aided Approach With Reduced Computational Overhead

The acquisition of channel state information (CSI) is essential in MIMO-OFDM communication systems. Data-aided enhanced receivers, by incorporating domain knowledge, effectively mitigate performance degradation caused by imperfect CSI, particularly in dynamic wireless environments. However, existing...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 73; no. 11; pp. 12100 - 12113
Main Authors: Li, Xinjie, Zhang, Jing, Zhou, Xingyu, Wen, Chao-Kai, Jin, Shi
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Channel estimation
Codes
Communications systems
Complexity
Complexity theory
Computational modeling
data-aided channel estimation
Detectors
Estimation
expectation propagation
iterative receiver
LMMSE
MIMO
MIMO communication
MIMO-OFDM
OFDM
Orthogonal Frequency Division Multiplexing
Performance degradation
Receivers
Signal detection
Subsystems
Symbols
Time-frequency analysis
ISSN:0090-6778, 1558-0857
Online Access:Get full text
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Summary:The acquisition of channel state information (CSI) is essential in MIMO-OFDM communication systems. Data-aided enhanced receivers, by incorporating domain knowledge, effectively mitigate performance degradation caused by imperfect CSI, particularly in dynamic wireless environments. However, existing methodologies face notable challenges: they either refine channel estimates within MIMO subsystems separately, which proves ineffective due to deviations from assumptions regarding the time-varying nature of channels, or fully exploit the time-frequency characteristics but incur significantly high computational overhead due to dimensional concatenation. To address these issues, this study introduces a novel data-aided method aimed at reducing complexity, particularly suited for fast-fading scenarios in fifth-generation (5G) and beyond networks. We derive a general form of a data-aided linear minimum mean-square error (LMMSE)-based algorithm, optimized for iterative joint channel estimation and signal detection. Additionally, we propose a computationally efficient alternative to this algorithm, which achieves comparable performance with significantly reduced complexity. Empirical evaluations reveal that our proposed algorithms outperform several state-of-the-art approaches across various MIMO-OFDM configurations, pilot sequence lengths, and in the presence of time variability. Comparative analysis with basis expansion model-based iterative receivers highlights the superiority of our algorithms in achieving an effective trade-off between accuracy and computational complexity.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2025.3591173