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.

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.

An audiogram records the hearing status, including each hearing threshold at multiple frequencies. While deep learning is gradually maturing in the clinical research approach, audiologists could speed up their diagnostic process with audiogram digitization for handwritten graphs or electronically generated images from instruments. However, given the diversity of audiogram symbols and formats, the existing audiogram digitization model has room for improvement in recognition accuracy. We propose a multi-stage workflow to enhance accuracy by integrating YOLOv5 and the optical character recognition (OCR) model. Our proposed audiogram digitization model could identify all audiogram symbols with an accuracy rate of 98%. We hope that this model could help future research in audiology.

Upcoming 6G wireless networks promise faster speeds, lower latency, and increased capacity. A key innovation is the intelligent reflecting surface (IRS), which enhances coverage, capacity, and energy efficiency. However, the complex training and computational costs associated with the IRS’s passive components pose challenges for channel prediction. We address this by applying the denoising method on raw data as well as multihead attention for discovery of hidden patterns in complex data, and then using sparse encoding in latent space to retain important information for capturing cross-domain features in the space, time, and frequency domain. Numerical results demonstrate significant performance improvements in channel prediction for IRS-assisted millimeter-wave MIMO OFDM systems.