Attention-Aided Autoencoder-Based Channel Prediction for Intelligent Reflecting Surface-Assisted Millimeter-Wave Communications

Abstract

Sixth-generation (6G) wireless communication networks will provide larger coverage and capacity with lower energy consumption and hardware costs than 5G. Intelligent reflecting surface (IRS)-aided millimeter-wave massive MIMO OFDM communication is a new technology that intelligently manipulates electromagnetic waves. This has recently attracted much attention given its potential to manage the wireless propagation environment at low hardware costs and with minimal energy usage. However, channel prediction is complicated by the fact that IRS is rarely equipped with power amplifiers, various radio frequency chains, or a significant number of reflecting components. In this paper, we propose a convolutional denoising autoencoder model and investigate a joint attention mechanism for channel prediction. Then, we employ the attention mechanism to identify features of channel subcarrier interference to improve the channel prediction performance. Long-range dependent specificity is captured through the attention mechanism to generate useful features from the input signal. The encoder-decoder design of the autoencoder serves as a dimensionality reduction method that enables the autoencoder to predict the spatial and temporal distribution features of continuous signals by exploiting the extraction of sequence features from the model. Numerical results show that the proposed algorithm significantly improves the performance of IRS-aided millimeter-wave massive MIMO OFDM communication systems compared with previous methods.