Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Cross Attention-aided Variational Autoencoder-based Precoding Design

Sixth-generation technology, which surpasses 5G with faster speeds, lower latency, and increased capacity, highlights the pivotal intelligent reflecting surface (IRS) and optimizes wireless communication by precisely handling incoming radio waves, resulting in enhanced network coverage, capacity, and energy efficiency. In real-world scenarios, however, hybrid precoding in the IRS is affected by noise and interference. To address this, we propose DVAE-CATT-Precoding (variational denoising autoencoder model with cross attention precoding), in which cross attention enhances channel precoding by reevaluating the significance of the time domain. A variational autoencoder (VAE) is trained with reconstruction and regularization losses for parameter optimization, and its learned latent space facilitates feature capture for intra-channel precoding in both the frequency and spatial domains. We use an autoencoder to reconstruct the latent space, improving our analog precoding module’s ability to capture global domain features. Additionally, a CNN models frequency and spatial domain intricacies for efficient channel precoding, reducing interference in the proposed digital precoding module. Numerical results show remarkable improvements in MSE, achievable rate, generalizability, and robustness compared to previous works.