A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modeling
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910880011124736 |
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| author | Guo, Keying He, Ruisi Yang, Mi Zhang, Yuxin Ai, Bo Zhang, Haoxiang Han, Jiahui Chen, Ruifeng |
| author_facet | Guo, Keying He, Ruisi Yang, Mi Zhang, Yuxin Ai, Bo Zhang, Haoxiang Han, Jiahui Chen, Ruifeng |
| contents | Time-varying non-stationary channels, with complex dynamic variations and temporal evolution characteristics, have significant challenges in channel modeling and communication system performance evaluation. Most existing methods of time-varying channel modeling focus on predicting channel state at a given moment or simulating short-term channel fluctuations, which are unable to capture the long-term evolution of the channel. This paper emphasizes the generation of long-term dynamic channel to fully capture evolution of non-stationary channel properties. The generated channel not only reflects temporal dynamics but also ensures consistent stationarity. We propose a hybrid deep learning framework that combines conditional generative adversarial networks (CGAN) with long short-term memory (LSTM) networks. A stationarity-constrained approach is designed to ensure temporal correlation of the generated time-series channel. This method can generate channel with required temporal non-stationarity. The model is validated by comparing channel statistical features, and the results show that the generated channel is in good agreement with raw channel and provides good performance in terms of non-stationarity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modeling Guo, Keying He, Ruisi Yang, Mi Zhang, Yuxin Ai, Bo Zhang, Haoxiang Han, Jiahui Chen, Ruifeng Signal Processing Machine Learning Time-varying non-stationary channels, with complex dynamic variations and temporal evolution characteristics, have significant challenges in channel modeling and communication system performance evaluation. Most existing methods of time-varying channel modeling focus on predicting channel state at a given moment or simulating short-term channel fluctuations, which are unable to capture the long-term evolution of the channel. This paper emphasizes the generation of long-term dynamic channel to fully capture evolution of non-stationary channel properties. The generated channel not only reflects temporal dynamics but also ensures consistent stationarity. We propose a hybrid deep learning framework that combines conditional generative adversarial networks (CGAN) with long short-term memory (LSTM) networks. A stationarity-constrained approach is designed to ensure temporal correlation of the generated time-series channel. This method can generate channel with required temporal non-stationarity. The model is validated by comparing channel statistical features, and the results show that the generated channel is in good agreement with raw channel and provides good performance in terms of non-stationarity. |
| title | A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modeling |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2503.13468 |