Ensemble learning aided large communication model for multi-scenario nonlinear distortion
In this paper, we propose the Large Communication Model (LCM), a deep neural network receiver designed specifically for orthogonal frequency-division multiplexing (OFDM) systems. Inspired by the Mixture of Experts (MoE) model, LCM incorporates ensemble learning within the Comm-Trans Net framework to...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/182828 |
| _version_ | 1826113997111296000 |
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| author | Xie, Yihang Liu, Xiaobei Su, Zhengyang Teh, Kah Chan Guan, Yong Liang Yang, Chaosan |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Xie, Yihang Liu, Xiaobei Su, Zhengyang Teh, Kah Chan Guan, Yong Liang Yang, Chaosan |
| author_sort | Xie, Yihang |
| collection | NTU |
| description | In this paper, we propose the Large Communication Model (LCM), a deep neural network receiver designed specifically for orthogonal frequency-division multiplexing (OFDM) systems. Inspired by the Mixture of Experts (MoE) model, LCM incorporates ensemble learning within the Comm-Trans Net framework to address the challenges posed by various nonlinear distortions in wireless communication environments. By utilizing ensemble methods, LCM achieves robust adaptation to diverse distortion scenarios without requiring specific prior domain knowledge of specific distortion types. This architecture enables LCM to dynamically adapt to complex distortion environments while maintaining high performance. Extensive evaluations demonstrate that LCM consistently outperforms traditional OFDM receivers, highlighting its effectiveness across a wide range of distortion conditions. Our findings emphasize the reliability and adaptability of LCM in maintaining near optimal communication performance. |
| first_indexed | 2025-03-09T11:00:17Z |
| format | Journal Article |
| id | ntu-10356/182828 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T11:00:17Z |
| publishDate | 2025 |
| record_format | dspace |
| spelling | ntu-10356/1828282025-03-07T15:45:34Z Ensemble learning aided large communication model for multi-scenario nonlinear distortion Xie, Yihang Liu, Xiaobei Su, Zhengyang Teh, Kah Chan Guan, Yong Liang Yang, Chaosan School of Electrical and Electronic Engineering Engineering Nonlinear distortion Orthogonal frequency-division multiplexing Ensemble learning Large communication model In this paper, we propose the Large Communication Model (LCM), a deep neural network receiver designed specifically for orthogonal frequency-division multiplexing (OFDM) systems. Inspired by the Mixture of Experts (MoE) model, LCM incorporates ensemble learning within the Comm-Trans Net framework to address the challenges posed by various nonlinear distortions in wireless communication environments. By utilizing ensemble methods, LCM achieves robust adaptation to diverse distortion scenarios without requiring specific prior domain knowledge of specific distortion types. This architecture enables LCM to dynamically adapt to complex distortion environments while maintaining high performance. Extensive evaluations demonstrate that LCM consistently outperforms traditional OFDM receivers, highlighting its effectiveness across a wide range of distortion conditions. Our findings emphasize the reliability and adaptability of LCM in maintaining near optimal communication performance. Ministry of Defence (MINDEF) Nanyang Technological University Submitted/Accepted version This work was supported in part by Temasek Laboratories@NTU Research Programme TLSP23-13, in part by Temasek Laboratories@NTU Signal Research Programme 4, and in part by Nanyang Technological University, Singapore. 2025-03-03T07:01:09Z 2025-03-03T07:01:09Z 2024 Journal Article Xie, Y., Liu, X., Su, Z., Teh, K. C., Guan, Y. L. & Yang, C. (2024). Ensemble learning aided large communication model for multi-scenario nonlinear distortion. IEEE Transactions On Cognitive Communications and Networking, 3524186-. https://dx.doi.org/10.1109/TCCN.2024.3524186 2332-7731 https://hdl.handle.net/10356/182828 10.1109/TCCN.2024.3524186 2-s2.0-85214110305 3524186 en DSOCL22291 TLSP23-13 IEEE Transactions on Cognitive Communications and Networking © 2024 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1109/TCCN.2024.3524186 application/pdf |
| spellingShingle | Engineering Nonlinear distortion Orthogonal frequency-division multiplexing Ensemble learning Large communication model Xie, Yihang Liu, Xiaobei Su, Zhengyang Teh, Kah Chan Guan, Yong Liang Yang, Chaosan Ensemble learning aided large communication model for multi-scenario nonlinear distortion |
| title | Ensemble learning aided large communication model for multi-scenario nonlinear distortion |
| title_full | Ensemble learning aided large communication model for multi-scenario nonlinear distortion |
| title_fullStr | Ensemble learning aided large communication model for multi-scenario nonlinear distortion |
| title_full_unstemmed | Ensemble learning aided large communication model for multi-scenario nonlinear distortion |
| title_short | Ensemble learning aided large communication model for multi-scenario nonlinear distortion |
| title_sort | ensemble learning aided large communication model for multi scenario nonlinear distortion |
| topic | Engineering Nonlinear distortion Orthogonal frequency-division multiplexing Ensemble learning Large communication model |
| url | https://hdl.handle.net/10356/182828 |
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