A GAN-GRU Based Space-Time Predictive Channel Model for 6G Wireless Communications

Li, Zheao; Wang, Cheng-Xiang; Huang, Chen; Huang, Jie; Li, Junling; Zhou, Wenqi; Chen, Yunfei

Abstract

The advent of sixth-generation (6 G) wireless communications has posed significant challenges to channel modeling. Channel measurements cannot cover all scenarios and frequency bands for 6 G, and conventional models lack accurate predictive capabilities. To address these issues, this paper proposes a novel 6 G space-time joint predictive channel model to predict channels in the space-time domains, which can rebuild lost measurement data and correct abnormal data. The proposed model designs a space-time generative adversarial network (STGAN) framework, conditioned on channel large-scale and small-scale characteristics, to synthesize sufficient space-time channel datasets, effectively overcoming data shortages. Accompanied by path identification and characteristic classification, the coupled gated recurrent unit (GRU) framework conducts precise predictions for unknown channels in the space-time domains. Comprehensive experiments demonstrate the proposed model's superiority over other methods, including the geometry-based stochastic channel model (GBSM), GRU, long short-term memory (LSTM), and radial basis function neural network (RBF-NN). The model's effectiveness can be attributed to its architecture to capture complex space-time variations and accurately predict non-linear channel characteristics based on continuous measurements. Validation on both indoor and outdoor channel measurements further confirms the model's generality and accuracy. The proposed model provides a robust solution in the space-time joint channel prediction for advanced wireless communications.

Citation

Li, Z., Wang, C.-X., Huang, C., Huang, J., Li, J., Zhou, W., & Chen, Y. (2024). A GAN-GRU Based Space-Time Predictive Channel Model for 6G Wireless Communications. IEEE Transactions on Vehicular Technology, 73(7), 9370-9386. https://doi.org/10.1109/TVT.2024.3367386

• Journal Article Type: Article
• Acceptance Date: Jan 28, 2024
• Online Publication Date: Mar 8, 2024
• Publication Date: 2024-07
• Deposit Date: Feb 9, 2024
• Publicly Available Date: Mar 15, 2024
• Journal: IEEE Transactions on Vehicular Technology
• Print ISSN: 0018-9545
• Electronic ISSN: 1939-9359
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 73
• Issue: 7
• Pages: 9370-9386
• DOI: https://doi.org/10.1109/TVT.2024.3367386
• Public URL: https://durham-repository.worktribe.com/output/2231652