# A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modeling

arXiv: https://arxiv.org/abs/2503.13468 · HTML: https://mlsanity.com/paper/2503.13468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities](https://mlsanity.com/paper/2008.03213/) (2020)
- [Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms](https://mlsanity.com/paper/2011.07466/) (2020)
- [Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling](https://mlsanity.com/paper/2111.12228/) (2021)
- [A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond](https://mlsanity.com/paper/2101.06610/) (2021)
- [Artificial intelligence enabled radio propagation for communications-Part I: Channel characterization and antenna-channel optimization](https://mlsanity.com/paper/2111.12227/) (2021)
- [COST CA20120 INTERACT Framework of Artificial Intelligence Based Channel Modeling](https://mlsanity.com/paper/2411.11798/) (2024)

## Abstract (arXiv)

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.