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24 June 2024
  » arxiv » 2302.00341

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Predicting CSI Sequences With Attention-Based Neural Networks
Valentina Rizzello ; Benedikt Böck ; Michael Joham ; Wolfgang Utschick ;
Date 1 Feb 2023
AbstractIn this work, we consider the problem of multi-step channel prediction in wireless communication systems. In existing works, autoregressive (AR) models are either replaced or combined with feed-forward neural networks(NNs) or, alternatively, with recurrent neural networks (RNNs). This paper explores the possibility of using sequence-to-sequence (Seq2Seq) and transformer neural network (TNN) models for channel state information (CSI) prediction. Simulation results show that both, Seq2Seq and TNNs, represent an appealing alternative to RNNs and feed-forward NNs in the context of CSI prediction. Additionally, the TNN with a few adaptations can extrapolate better than other models to CSI sequences that are either shorter or longer than the ones the model saw during training.
Source arXiv, 2302.00341
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