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

The advent of sixth-generation (6G) wireless communications has posed significant challenges to channel modeling. Channel measurements cannot cover all scenarios and frequency bands for 6G, and conventional models lack accurate predictive capabilities. To address these issues, this paper proposes a novel 6G 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.