Search Results for author:
Found 33 papers, 8 papers with code
Rydberg Atomic Quantum Receivers for Multi-Target DOA Estimation
To harness the advantages and exploit the potential of RAQRs in wireless sensing, we investigate the realization of the direction of arrival (DOA) estimation by RAQRs.
GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks
We build GDSG as a multi-task diffusion model utilizing a Graph Neural Network (GNN) to acquire the distribution of high-quality solutions.
Rydberg Atomic Quantum Receivers for Classical Wireless Communications and Sensing: Their Models and Performance
Our scheme and model provide explicit design guidance to RAQR-aided wireless systems.
Diffusion Models as Network Optimizers: Explorations and Analysis
Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization.
Rydberg Atomic Quantum Receivers for Classical Wireless Communication and Sensing
Finally, we conceive Rydberg atomic quantum single-input single-output (RAQ-SISO) and multiple-input multiple-output (RAQ-MIMO) schemes for facilitating the integration of RAQRs with classical wireless systems, and conclude with a set of potent research directions.
DiffSG: A Generative Solver for Network Optimization with Diffusion Model
In summary, we demonstrate the potential of diffusion generative models in tackling complex network optimization problems and outline a promising path for their broader application in the communication community.
Strategic Federated Learning: Application to Smart Meter Data Clustering
In this paper, we introduce a novel FL framework in which the FC uses an aggregate version of the MI to make decisions that affect the client's utility functions.
Object-Attribute-Relation Representation based Video Semantic Communication
In this paper, we introduce the use of object-attribute-relation (OAR) as a semantic framework for videos to facilitate low bit-rate coding and enhance the JSCC process for more effective video transmission.
Exploring Channel Estimation and Signal Detection for ODDM-based ISAC Systems
Inspired by providing reliable communications for high-mobility scenarios, in this letter, we investigate the channel estimation and signal detection in integrated sensing and communication~(ISAC) systems based on the orthogonal delay-Doppler multiplexing~(ODDM) modulation, which consists of a pulse-train that can achieve the orthogonality with respect to the resolution of the delay-Doppler~(DD) plane.
Generative AI for Immersive Communication: The Next Frontier in Internet-of-Senses Through 6G
Over the past two decades, the Internet-of-Things (IoT) has become a transformative concept, and as we approach 2030, a new paradigm known as the Internet of Senses (IoS) is emerging.
Near-Field Channel Modeling for Holographic MIMO Communications
Empowered by the latest progress on innovative metamaterials/metasurfaces and advanced antenna technologies, holographic multiple-input multiple-output (H-MIMO) emerges as a promising technology to fulfill the extreme goals of the sixth-generation (6G) wireless networks.
Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
Despite the impressive performance of LLMs, their widespread adoption faces challenges due to substantial computational and memory requirements during inference.
The Falcon Series of Open Language Models
We report detailed evaluations, as well as a deep dive into the methods and custom tooling employed to pretrain Falcon.
Ranked #21 on
Sentence Completion
on HellaSwag
Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems
In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game~(MFG).
A Transmit-Receive Parameter Separable Electromagnetic Channel Model for LoS Holographic MIMO
To support the extremely high spectral efficiency and energy efficiency requirements, and emerging applications of future wireless communications, holographic multiple-input multiple-output (H-MIMO) technology is envisioned as one of the most promising enablers.
Holographic MIMO Communications with Arbitrary Surface Placements: Near-Field LoS Channel Model and Capacity Limit
Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits.
A Generalized Electromagnetic-Domain Channel Modeling for LOS Holographic MIMO with Arbitrary Surface Placements
Holographic multiple-input multiple-output (H-MIMO) is considered as one of the most promising technologies to enable future wireless communications in supporting the expected extreme requirements, such as high energy and spectral efficiency.
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems.
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces
The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives.
C-GRBFnet: A Physics-Inspired Generative Deep Neural Network for Channel Representation and Prediction
Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths.
Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges
New technological advancements in wireless networks have enlarged the number of connected devices.
Semantics-Native Communication with Contextual Reasoning
Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across many tasks.
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connected workers.
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond.
Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications
We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station.
Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends
Future wireless networks are expected to evolve towards an intelligent and software reconfigurable paradigm enabling ubiquitous communications between humans and mobile devices.
Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces
Reconfigurable Intelligent Surfaces (RISs) comprised of tunable unit elements have been recently considered in indoor communication environments for focusing signal reflections to intended user locations.
Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems
We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase.
Wireless Network Intelligence at the Edge
), requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML).
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment.
Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?
Numerical and analytical results show that the maximal EE is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve a relatively large number of users using ZF processing.
Information Theory Networking and Internet Architecture Information Theory
A New Look at Dual-Hop Relaying: Performance Limits with Hardware Impairments
Such approximations make sense in low-rate systems, but can lead to very misleading results when analyzing future high-rate systems.
Information Theory Information Theory
Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits
The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain.
Information Theory Information Theory
