Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging
2024 IEEE Wireless Communications and Networking Conference (WCNC)(2024)
摘要
Reconfigurable intelligent surfaces (RISs) have emerged as a promising
technology to enhance the performance of sixth-generation (6G) and beyond
communication systems. The passive nature of RISs and their large number of
reflecting elements pose challenges to the channel estimation process. The
associated complexity further escalates when the channel coefficients are
fast-varying as in scenarios with user mobility. In this paper, we propose an
extended channel estimation framework for RIS-assisted multiple-input
multiple-output (MIMO) systems based on a convolutional neural network (CNN)
integrated with an autoregressive (AR) predictor. The implemented framework is
designed for identifying the aging pattern and predicting enhanced estimates of
the wireless channels in correlated fast-fading environments. Insightful
simulation results demonstrate that our proposed CNN-AR approach is robust to
channel aging, exhibiting a high-precision estimation accuracy. The results
also show that our approach can achieve high spectral efficiency and low pilot
overhead compared to traditional methods.
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关键词
Reconfigurable intelligent surfaces,channel estimation,channel aging,Autoregressive processes,CNN
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