Self-attention-based multidimensional feature aggregation neural network for OFDM channel estimation

Yan Sun; Yihang Luo

doi:10.1117/12.3054308

3 January 2025 Self-attention-based multidimensional feature aggregation neural network for OFDM channel estimation

Yan Sun, Yihang Luo

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134421A (2025) https://doi.org/10.1117/12.3054308
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

In this paper, we propose a multi-dimensional feature aggregation channel estimation network (FACENet) based on self-attention to improve pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM) systems. This network aggregates spatial and channel features of the input data by alternately employing spatial self-attention and channel self-attention, then processes these features through a deep residual network. Given the strong time and frequency correlation of the channel, the spatial self-attention extracts and integrates spatial features from both time and frequency direction. Additionally, to support flexible pilot patterns and maximize the utilization of pilot signals, we propose an interpolation scheme based on data-pilot aided (DPA) estimation, with the interpolation results serving as input to FACENet. Simulation results show that FACENet outperforms other comparative methods across different modulation schemes and pilot numbers. Furthermore, FACENet has been shown to exhibit good robustness, making it applicable to receivers with varying speeds and other channel scenarios.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yan Sun and Yihang Luo "Self-attention-based multidimensional feature aggregation neural network for OFDM channel estimation", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134421A (3 January 2025); https://doi.org/10.1117/12.3054308

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