Related papers: Deep Learning-Enabled Multi-Tag Detection in Ambie…
Ambient backscatter communication (AmBC) enables wireless-powered backscatter devices (BDs) to transmit information over ambient radio-frequency (RF) carriers without using an RF transmitter, and thus has emerged as a promising technology…
Ambient backscatter communication is a newly emerged paradigm, which utilizes the ambient radio frequency (RF) signal as the carrier to reduce the system battery requirement, and is regarded as a promising solution for enabling large scale…
Ambient backscatter communication technology (AmBC) and a novel device category called zero-energy devices (ZED) have recently emerged as potential components for the forthcoming 6th generation (6G) networks. A ZED communicates with a…
Future 6G networks envisions to blur the line between communication and sensing, leveraging ubiquitous OFDM waveforms for both high throughput data and environmental awareness. In this work, we do a thorough analysis of Communication based…
This letter presents a pioneering method that employs deep learning within a probabilistic framework for the joint estimation of both direct and cascaded channels in an ambient backscatter (AmBC) network comprising multiple tags. In…
As wireless communication systems evolve, automatic modulation recognition (AMR) plays a key role in improving spectrum efficiency, especially in cognitive radio systems. Traditional AMR methods face challenges in complex, noisy…
Backscatter is a key technology for battery-free sensing in industrial IoT applications. To fully cover numerous tags in the deployment area, one often needs to deploy multiple readers, each of which communicates with tags within its…
Automotive-Industry 5.0 will use beyond fifth-generation (B5G) technologies to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other devices. Recently, ambient…
This paper presents a novel symbiotic radio system for integrated sensing and backscatter communication (ISABC) technique that enables signal-domain interference-free coexistence of the primary communication signal and the backscatter…
Future Internet-of-Things networks are envisioned to use small and cheap sensor nodes with extremely low power consumption to avoid the extensive use of batteries. To provide connectivity to a massive number of these nodes, backscatter…
In this study, a modular, data-free pipeline for multi-label intention recognition is proposed for agentic AI applications in transportation. Unlike traditional intent recognition systems that depend on large, annotated corpora and often…
In this paper, we present an ambient backscatters-friendly 5G network that creates locations with large power (hot spots) for tags and good reception locations (quiet spots or coherent spots) for readers. The massive multiple input multiple…
Accurate channel state information (CSI) is critical for realizing the full potential of multiple-antenna wireless communication systems. While deep learning (DL)-based CSI feedback methods have shown promise in reducing feedback overhead,…
This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver…
Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose. Linguistic features, mainly from parsers, have been used to detect MCI, but this is not suitable for large-scale assessments. MCI disfluencies produce…
To improve the data rate in differential chaos shift keying (DCSK) based ambient backscatter communication (AmBC) system, we propose a new AmBC system based on code index modulation (CIM), referred to as CIM-DCSK-AmBC system. In the…
Efficient spectrum utilization is critical to meeting the growing data demands of modern wireless communication networks. Automatic Modulation Classification (AMC) plays a key role in enhancing spectrum efficiency by accurately identifying…
Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the…
Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal…
Considering the exponential growth of Internet-of-Things devices and the goals toward sustainable networks, the complexity should be focused on the infrastructure side. For a massive number of passive devices, backscatter communication (BC)…