In this paper, we explore the potential of artificial intelligence (AI) to address the challenges
posed by terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) systems. We …

Deep learning is envisioned to facilitate the operation of wireless receivers, with emerging
architectures integrating deep neural networks (DNNs) with traditional modular receiver …

This study proposes a passive human identification system based on wireless signals. The
proposed system comprises three phases; preprocessing and standardization of recorded …

For multiple-input multiple-output systems, channel prediction is crucial for mitigating
channel aging in mobile scenarios. The existing channel prediction schemes typically …

High-speed train (HST) communication has emerged as a prominent area of research, given
its critical role in meeting the demanding requirements of high mobility scenarios like auto …

Deep neural networks (DNNs) were shown to facilitate the operation of uplink multiple-input
multiple-output (MIMO) receivers, with emerging architectures augmenting modules of …

Wireless localization and sensing are increasingly important capabilities when the networks
are evolving towards the 6^th generation era. While the physics-inspired geometrical …

Channel prediction is an effective approach for reducing the feedback or estimation
overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel …

In the future sixth-generation (6G) era, to support accurate localization sensing and efficient
communication link establishment for intelligent agents, a comprehensive understanding of …

Deep Learning (DL)-based channel prediction has emerged as a complementary solution to
Channel State Information (CSI) obsolescence in the context of beyond 5G networks. For …