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

TL;DR

This work tackles joint channel estimation and data detection in MIMO-OFDM under fast time variation. It develops a data-aided LMMSE framework (MJCD-LMMSE) and a low-complexity equivalent (OJCD-LMMSE) that leverage detected data symbols via an EP detector. The proposed methods provide closed-form Wiener-filter based channel estimation with refined statistics and demonstrate performance gains over state-of-the-art approaches across various MIMO-OFDM configurations, pilot lengths, and mobility, while reducing complexity by several orders of magnitude. The results offer a practical JCD receiver design for 5G and beyond that balances estimation accuracy and computational load in time-selective fading.

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.

Figures (10)

• Figure 1: Block diagram of MIMO-OFDM receiver with traditional LMMSE channel estimation and EP signal detection.
• Figure 2: Illustration of MIMO-OFDM receiver with the proposed JCD structure and channel decoder.
• Figure 3: Frame structures under different scenarios.
• Figure 4: Accuracy of channel estimation and signal detection at the output of JCD structure under different settings of $I$.
• Figure 5: Detection performance comparison of JCD design using proposed algorithms.