Intelligent Reflecting Surface Enhanced Wireless Communications With MultiHead-Attention Sparse Autoencoder-Based Channel Prediction
Journal
IEEE Communications Letters
Date Issued
2023-01-01
Author(s)
Abstract
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.
Subjects
channel prediction | denoising sparse autoencoder millimeter-wave | Frequency-domain analysis | Head | Intelligent reflecting surface (IRS) | multi-head attention | Noise reduction | OFDM | Radio frequency | sixth generation (6G) | Symbols | Wireless communication
SDGs
Type
journal article