相关论文: Multiport Network Theory for Modeling and Optimizi…
This Letter addresses the physics-consistent optimization of reconfigurable intelligent surfaces (RISs) with mutual coupling (MC) and 1-bit-programmable RIS elements. This combination of constraints is typical of current prototypes but…
Stacked intelligent metasurfaces (SIMs) extend the concept of reconfigurable intelligent surfaces by cascading multiple programmable layers, enabling advanced electromagnetic wave transformations for communication and sensing applications.…
In this paper, we consider a reconfigurable intelligent surface (RIS) and model it by using multiport network theory. We first compare the representation of RIS by using $Z$-parameters and $S$-parameters, by proving their equivalence and…
Sensing the direction of arrival and polarization of impinging signals is a key prerequisite for beamforming and interference mitigation in modern wireless communication systems. Dynamic metasurface antennas (DMAs) can multiplex direction-…
As a novel approach to flexibly adjust the wireless environment, reconfigurable intelligent surfaces (RIS) have shown significant application potential across various domains, including wireless communication, radar detection, and the…
Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM)…
Metasurfaces have emerged as transformative electromagnetic structures for wireless communications, enabling the real-time control over wave propagation, yielding potential for improved data rates, privacy, energy efficiency and even…
Multiport network theory has been proved to be a suitable abstraction model for analyzing and optimizing reconfigurable intelligent surfaces (RISs) in an electromagnetically consistent manner, especially for studying the impact of the…
We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of knowledge transfer. MNMT is more promising…
Reconfigurable Intelligent Surface (RIS) modeling and optimization are a crucial steps in developing the next generation of wireless communications. To this aim, the availability of accurate electromagnetic (EM) models is of paramount…
We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer…
Multi-target tracking (MTT) is a classical signal processing task, where the goal is to estimate the states of an unknown number of moving targets from noisy sensor measurements. In this paper, we revisit MTT from a deep learning…
Most use cases of reconfigurable antennas require an accurate forward model mapping configuration to radiated field (and reflections at feeds). Emerging dynamic metasurface antennas (DMAs) confront the conventional approach of extracting…
Deep learning, as a highly efficient method for metasurface inverse design, commonly use simulation data to train deep neural networks (DNNs) that can map desired functionalities to proper metasurface designs. However, the assumptions and…
Physics-consistent theoretical studies on RIS-parametrized wireless channels use models from multiport-network theory (MNT) to capture mutual-coupling (MC) effects. However, in practice, RIS design and radio environment are partially or…
We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domains as distinct languages and use…
Considering the rapid progress of theory, design, fabrication and applications, metasurface (MTS) has become a new research frontier in microwave, terahertz and optical bands. Reconfigurable metasurface (R-MTS) can dynamically modulate…
Our current world is revolutionned by the networks which are interconnecting any machine to any other one. Nowadays equipments are plugged to the network by the way of many different network adapters (Ethernet, wifi, GSM), and network…
Networks are a powerful tool to model complex systems, and the definition of many Graph Neural Networks (GNN), Deep Learning algorithms that can handle networks, has opened a new way to approach many real-world problems that would be hardly…
Representation learning of networks has witnessed significant progress in recent times. Such representations have been effectively used for classic network-based machine learning tasks like node classification, link prediction, and network…