Demonstrating Interoperable Channel State Feedback Compression with Machine Learning
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913914825998336 |
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| author | Korpi, Dani Wang, Rachel Wang, Jerry Ibrahim, Abdelrahman Nuzman, Carl Wang, Runxin Mestav, Kursat Rasim Zhang, Dustin Saniee, Iraj Winston, Shawn Pavlovic, Gordana Ding, Wei Hillery, William J. Hao, Chenxi Thirunagari, Ram Chang, Jung Kim, Jeehyun Kozicki, Bartek Samardzija, Dragan Yoo, Taesang Maeder, Andreas Ji, Tingfang Viswanathan, Harish |
| author_facet | Korpi, Dani Wang, Rachel Wang, Jerry Ibrahim, Abdelrahman Nuzman, Carl Wang, Runxin Mestav, Kursat Rasim Zhang, Dustin Saniee, Iraj Winston, Shawn Pavlovic, Gordana Ding, Wei Hillery, William J. Hao, Chenxi Thirunagari, Ram Chang, Jung Kim, Jeehyun Kozicki, Bartek Samardzija, Dragan Yoo, Taesang Maeder, Andreas Ji, Tingfang Viswanathan, Harish |
| contents | Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Various simulation-based studies have shown that ML-based feedback compression can result in reduced overhead and more accurate channel information. However, to the best of our knowledge, there are no real-life proofs of concepts demonstrating the benefits of ML-based channel feedback compression in a practical setting, where the user equipment (UE) and base station have no access to each others' ML models. In this paper, we present a novel approach for training interoperable compression and decompression ML models in a confidential manner, and demonstrate the accuracy of the ensuing models using prototype UEs and base stations. The performance of the ML-based channel feedback is measured both in terms of the accuracy of the reconstructed channel information and achieved downlink throughput gains when using the channel information for beamforming. The reported measurement results demonstrate that it is possible to develop an accurate ML-based channel feedback link without having to share ML models between device and network vendors. These results pave the way for a practical implementation of ML-based channel feedback in commercial 6G networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21796 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Demonstrating Interoperable Channel State Feedback Compression with Machine Learning Korpi, Dani Wang, Rachel Wang, Jerry Ibrahim, Abdelrahman Nuzman, Carl Wang, Runxin Mestav, Kursat Rasim Zhang, Dustin Saniee, Iraj Winston, Shawn Pavlovic, Gordana Ding, Wei Hillery, William J. Hao, Chenxi Thirunagari, Ram Chang, Jung Kim, Jeehyun Kozicki, Bartek Samardzija, Dragan Yoo, Taesang Maeder, Andreas Ji, Tingfang Viswanathan, Harish Signal Processing Artificial Intelligence Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Various simulation-based studies have shown that ML-based feedback compression can result in reduced overhead and more accurate channel information. However, to the best of our knowledge, there are no real-life proofs of concepts demonstrating the benefits of ML-based channel feedback compression in a practical setting, where the user equipment (UE) and base station have no access to each others' ML models. In this paper, we present a novel approach for training interoperable compression and decompression ML models in a confidential manner, and demonstrate the accuracy of the ensuing models using prototype UEs and base stations. The performance of the ML-based channel feedback is measured both in terms of the accuracy of the reconstructed channel information and achieved downlink throughput gains when using the channel information for beamforming. The reported measurement results demonstrate that it is possible to develop an accurate ML-based channel feedback link without having to share ML models between device and network vendors. These results pave the way for a practical implementation of ML-based channel feedback in commercial 6G networks. |
| title | Demonstrating Interoperable Channel State Feedback Compression with Machine Learning |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2506.21796 |