Related papers: A Learnable SIM Paradigm: Fundamentals, Training T…
Recent advances in Wireless Physical Layer Foundation Models (WPFMs) promise a new paradigm of universal Radio Frequency (RF) representations. However, these models inherit critical limitations found in deep learning such as the lack of…
Motivated by the need for increased spectral efficiency and the proliferation of intelligent applications, the sixth-generation (6G) mobile network is anticipated to integrate the dual-functions of communication and sensing (C&S). Although…
Introduced with the advent of statistical wireless channel models for high mobility communications and having a profound role in communication-centric (CC) integrated sensing and communications (ISAC), the doubly-dispersive (DD) channel…
Deep learning is driving a radical paradigm shift in wireless communications, all the way from the application layer down to the physical layer. Despite this, there is an ongoing debate as to what additional values artificial intelligence…
The advent of sixth-generation (6G) places intelligence at the core of wireless architecture, fusing perception, communication, and computation into a single closed-loop. This paper argues that large artificial intelligence models (LAMs)…
The primary focus of Artificial Intelligence/Machine Learning (AI/ML) integration within the wireless technology is to reduce capital expenditures, optimize network performance, and build new revenue streams. Replacing traditional…
Reconfigurable intelligent surfaces enhance wireless systems by reshaping propagation environments. However, dynamic metasurfaces (MSs) with numerous phase-shift elements incur undesired control and hardware costs. In contrast, static MSs…
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional…
With the rapid development of smart terminals and infrastructures, as well as diversified applications (e.g., virtual and augmented reality, remote surgery and holographic projection) with colorful requirements, current networks (e.g., 4G…
Extremely large-scale arrays (XL-arrays) and ultra-high frequencies are two key technologies for sixth-generation (6G) networks, offering higher system capacity and expanded bandwidth resources. To effectively combine these technologies, it…
Transmission through reconfigurable intelligent surfaces (RISs), which control the reflection/scattering characteristics of incident waves in a deliberate manner to enhance the signal quality at the receiver, appears as a promising…
Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G)…
Intelligent surfaces that reshape electromagnetic waves are regarded as disruptive technologies for wireless networks. However, existing designs sit at two costly extremes: dynamic reconfigurable intelligent surfaces (RISs) offer fine beam…
Indoor wireless simultaneous localization and mapping (SLAM) is considered as a promising technique to provide positioning services in future 6G systems. However, the accuracy of traditional wireless SLAM system heavily relies on the…
Diffractive deep neural network (D2NN), also referred to as reconfigurable intelligent metasurface based deep neural networks (Rb-DNNs) or stacked intelligent metasurfaces (SIMs) in the field of wireless communications, has emerged as a…
The emergence of multimodal foundation models has revolutionized learning paradigms by enabling joint understanding across diverse data types. In the context of next-generation wireless networks, integrating sensing and communication…
The far-field channel model has historically been used in wireless communications due to the simplicity of mathematical modeling and convenience for algorithm design. With the need for high data rates, low latency, and ubiquitous…
Software-defined metasurfaces (SDMs) comprise a dense topology of basic elements called meta-atoms, exerting the highest degree of control over surface currents among intelligent panel technologies. As such, they can transform impinging…
This vision paper addresses the research challenges of integrating traditional signal processing with Artificial Intelligence (AI) to enable energy-efficient, programmable, and scalable wireless connectivity infrastructures. While prior…
Refractive metasurfaces (RMTSs) offer a promising solution to improve energy efficiency of wireless systems. To address the limitations of single-layer RMTS, stacked intelligent metasurfaces (SIMs), which form the desired precoder and…