A Frequency Domain Predictive Channel Model for 6G Wireless MIMO Communications Based on Deep Learning

Abstract

The development of sixth-generation (6G) wireless communication systems brings significant challenges in channel modeling. Conducting channel measurements for 6G communications is highly expensive and cannot cover all scenarios and frequency bands. Moreover, existing conventional channel models fail to accurately predict channel characteristics in unknown frequency band. As a result, predictive channel modeling has emerged as a promising solution for addressing these challenges in 6G channel modeling. In this study, we propose a frequency domain predictive channel model that combines an autoencoder with a coupling Convolution Gated Recurrent Unit (Conv-GRU) cells. The proposed model aims to predict channel characteristics in unknown frequency bands. The proposed predictive channel model is validated by using data collected from multiple frequency bands channel measurements. To evaluate its performance, several commonly used prediction networks, i.e., a general LSTM network, a GRU-based predictive network, and a Conv-LSTM-based predictive network, are conducted as benchmarks for comparison. Based on evaluation results, our proposed predictive channel model achieves the highest level of accuracy in predicting channels. Additionally, we provide a performance bound for extrapolation predictability using a Ray Tracing simulator.