Generative and Explainable AI for High-Dimensional Channel Estimation
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909580002328576 |
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| author | Nguyen, Nghia Thinh Do, Tri Nhu |
| author_facet | Nguyen, Nghia Thinh Do, Tri Nhu |
| contents | In this paper, we propose a new adversarial training framework to address high-dimensional instantaneous channel estimation in wireless communications. Specifically, we train a generative adversarial network to predict a channel realization in the time-frequency-space domain, in which the generator exploits the third-order moment of the input in its loss function and applies a new reparameterization method for latent distribution learning to minimize the Wasserstein distance between the true and estimated channel distributions. Next, we propose an explainable artificial intelligence mechanism to examine how the critic discriminates the generated channel. We demonstrate that our proposed framework is superior to existing methods in terms of minimizing estimation errors. Additionally, we find that the critic's attention focuses on the high-power portion of the channel's time-frequency representation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_10775 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative and Explainable AI for High-Dimensional Channel Estimation Nguyen, Nghia Thinh Do, Tri Nhu Signal Processing In this paper, we propose a new adversarial training framework to address high-dimensional instantaneous channel estimation in wireless communications. Specifically, we train a generative adversarial network to predict a channel realization in the time-frequency-space domain, in which the generator exploits the third-order moment of the input in its loss function and applies a new reparameterization method for latent distribution learning to minimize the Wasserstein distance between the true and estimated channel distributions. Next, we propose an explainable artificial intelligence mechanism to examine how the critic discriminates the generated channel. We demonstrate that our proposed framework is superior to existing methods in terms of minimizing estimation errors. Additionally, we find that the critic's attention focuses on the high-power portion of the channel's time-frequency representation. |
| title | Generative and Explainable AI for High-Dimensional Channel Estimation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.10775 |