Deep OFDM Channel Estimation: Capturing Frequency Recurrence
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916087200743424 |
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| author | Jameel, Abu Shafin Mohammad Mahdee Malhotra, Akshay Gamal, Aly El Hamidi-Rad, Shahab |
| author_facet | Jameel, Abu Shafin Mohammad Mahdee Malhotra, Akshay Gamal, Aly El Hamidi-Rad, Shahab |
| contents | In this paper, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential behavior of channels across frequencies. Utilizing the fact that wireless channels have a high degree of correlation across frequencies, we employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints typically associated with recurrence based methods. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques and the performance has been validated on a wide range of 3rd Generation Partnership Project (3GPP) compliant channel scenarios at multiple signal-to-noise ratios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05436 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep OFDM Channel Estimation: Capturing Frequency Recurrence Jameel, Abu Shafin Mohammad Mahdee Malhotra, Akshay Gamal, Aly El Hamidi-Rad, Shahab Signal Processing Machine Learning Networking and Internet Architecture In this paper, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential behavior of channels across frequencies. Utilizing the fact that wireless channels have a high degree of correlation across frequencies, we employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints typically associated with recurrence based methods. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques and the performance has been validated on a wide range of 3rd Generation Partnership Project (3GPP) compliant channel scenarios at multiple signal-to-noise ratios. |
| title | Deep OFDM Channel Estimation: Capturing Frequency Recurrence |
| topic | Signal Processing Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2401.05436 |