Technology Category
Search and Optimization: Learning to SearchMachine Learning: Time-Series/Data StreamsIntelligent Robots: State Estimation
Joint Channel Estimation and Signal Detection for MIMO-OFDM: A Novel Data-Aided Approach with Reduced Computational Overhead
🤖 AI Summary
To address inaccurate channel state information (CSI) estimation and degraded detection performance caused by time-varying channels in fast-fading MIMO-OFDM systems for 5G and beyond, this paper proposes a data-aided joint channel estimation and signal detection method. We first formulate a generic data-aided linear minimum mean square error (LMMSE) framework tailored for iterative joint optimization, and then design a low-complexity surrogate algorithm leveraging time-frequency domain modeling and low-dimensional parametric approximation to achieve a superior trade-off between estimation accuracy and computational cost. Experimental results demonstrate that, across diverse MIMO configurations, pilot lengths, and time-varying channel conditions, the proposed method improves detection accuracy by 15–22% over state-of-the-art basis expansion model (BEM)-based receivers, while reducing computational complexity by a factor of 3.8.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Joint channel estimation and signal detection in MIMO-OFDM systems
Reducing computational overhead in fast-fading 5G environments
Balancing accuracy and complexity in iterative data-aided algorithms
Innovation
Methods, ideas, or system contributions that make the work stand out.
Data-aided LMMSE algorithm for joint estimation
Computationally efficient alternative with reduced complexity
Optimized for fast-fading 5G scenarios
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