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Found 84 papers, 7 papers with code
CAPA: Continuous-Aperture Arrays for Revolutionizing 6G Wireless Communications
In this paper, a novel continuous-aperture array (CAPA)-based wireless communication architecture is proposed, which relies on an electrically large aperture with a continuous current distribution.
STAR-RIS in Cognitive Radio Networks
The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system.
STAR-RIS Aided Integrated Sensing, Computing, and Communication for Internet of Robotic Things
A computation rate maximization problem is formulated to optimize the sensing and receive beamformers at the BS and the STAR-RIS coefficients under the BS power constraint, the sensing signal-to-noise ratio constraint, and STAR-RIS coefficients constraints.
Deep Learning for Beamforming in Multi-User Continuous Aperture Array (CAPA) Systems
A DeepCAPA (Deep Learning for Continuous Aperture Array (CAPA)) framework is proposed to learn beamforming in CAPA systems.
Performance of Linear Receive Beamforming for Continuous Aperture Arrays (CAPAs)
i) For MRC beamforming, a closed-form expression for the beamformer is derived to maximize per-user signal power, and the achieved uplink rate and mean-square error (MSE) in detecting received data symbols are analyzed.
WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models
Finally, we merge the synthetic samples that pass quality confirmation with the collected samples to obtain the WorkflowBench.
Generative Artificial Intelligence (GAI) for Mobile Communications: A Diffusion Model Perspective
First, a DM-driven communication architecture is proposed, which introduces two key paradigms, i. e., conditional DM and DM-driven deep reinforcement learning (DRL), for wireless data generation and communication management, respectively.
A Homogeneous Graph Neural Network for Precoding and Power Allocation in Scalable Wireless Networks
Lastly, using ICGNN as the core algorithm, we tailor the neural network's input and output for specific problem requirements and validate its performance in two scenarios: 1) in cellular networks, we develop a matrix-inverse-free multi-user multi-input multi-output (MU-MIMO) precoding scheme using the conjugate gradient (CG) method, adaptable to varying user and antenna numbers; 2) in a cell-free network, facing dynamic variations in the number of users served by APs, the number of APs serving each user, and the number of antennas per AP, we propose a universal power allocation scheme.
Symbiotic Sensing and Communication: Framework and Beamforming Design
By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system.
Diversity and Multiplexing for Continuous Aperture Array (CAPA)-Based Communications
2) For MIMO channels, high-SNR approximations are derived for the ADR and OP, based on which the DMT and associated array gain are revealed.
Multi-User Continuous-Aperture Array Communications: How to Learn Current Distribution?
The continuous aperture array (CAPA) can provide higher degree-of-freedom and spatial resolution than the spatially discrete array (SDPA), where optimizing multi-user current distributions in CAPA systems is crucial but challenging.
Near-Field Sensing: A Low-Complexity Wavenumber-Domain Method
A novel low-complexity wavenumber-domain method is proposed for near-field sensing (NISE).
Near-Field Sensing Enabled Predictive Beamforming: From Estimation to Tracking
It is also revealed that: 1)the proposed AGD-AO can achieve stable descending with small gradients, thereby accelerating convergence; 2) compared to far-field predictive beamforming and feedback-based schemes, both of the proposed methods exhibit superior performance; and 3) by incorporating multiple CPIs, the EKF method exhibits greater robustness in low SNR regions.
Accelerating Mobile Edge Generation (MEG) by Constrained Learning
A novel accelerated mobile edge generation (MEG) framework is proposed for generating high-resolution images on mobile devices.
Mobile Edge Generation-Enabled Digital Twin: Architecture Design and Research Opportunities
Based on the proposed protocols, the convergence between MEG and DT are highlighted.
Hybrid Beamforming Design for Near-Field ISAC with Modular XL-MIMO
Considering the hybrid digital-analog structure inherent to modular arrays, we formulate a joint analog-digital beamforming design problem based on the communication spectral efficiency and sensing signal-to-clutter-plus-noise ratio (SCNR).
Near-Field Localization and Sensing with Large-Aperture Arrays: From Signal Modeling to Processing
The signal processing community is currently witnessing a growing interest in near-field signal processing, driven by the trend towards the use of large aperture arrays with high spatial resolution in the fields of communication, localization, sensing, imaging, etc.
Sustainable Wireless Networks via Reconfigurable Intelligent Surfaces (RISs): Overview of the ETSI ISG RIS
Reconfigurable Intelligent Surfaces (RISs) are a novel form of ultra-low power devices that are capable to increase the communication data rates as well as the cell coverage in a cost- and energy-efficient way.
On the Performance of Continuous Aperture Array (CAPA)-Based Wireless Communications
The performance of continuous aperture array (CAPA)-based wireless communications is analyzed in an uplink scenario.
Aperture Selection for CAP Arrays (CAPAs)
The achieved performance is analyzed in an uplink scenario by considering both line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios.
Cramer-Rao Bounds for Near-Field Sensing: A Generic Modular Architecture
A generic modular array architecture is proposed, featuring uniform/non-uniform subarray layouts that allows for flexible deployment.
Active Simultaneously Transmitting and Reflecting Surface Assisted NOMA Networks
The numerical results are presented and show that: 1) ASTARS-NOMA with pSIC outperforms ASTARS assisted-orthogonal multiple access (ASTARS-OMA) in terms of outage probability and ergodic data rate; 2) The outage probability of ASTARS-NOMA can be further reduced within a certain range by increasing the power amplification factors; 3) The system throughputs of ASTARS-NOMA are superior to that of ASTARS-OMA in both delay-limited and delay-tolerant transmission modes.
Near-Field Communications: A Comprehensive Survey
Throughout this paper, promising directions are highlighted to inspire future research endeavors in the realm of NFC, underscoring its significance in the advancement of wireless communication technologies.
Autosen: improving automatic wifi human sensing through cross-modal autoencoder
AutoSen establishes a direct link between amplitude and phase through automated cross-modal autoencoder learning.
A unified framework for STAR-RIS coefficients optimization
Due to the lack of a unified comparison of communication systems equipped with different modes of STAR-RIS and the performance degradation caused by the constraints involving discrete selection, this paper proposes a unified optimization framework for handling the STAR-RIS operating mode and discrete phase constraints.
Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
We decompose the query video information into a clip prototype and a memory prototype for capturing local and long-term internal temporal guidance, respectively.
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission.
Caching-at-STARS: the Next Generation Edge Caching
As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm.
Successive Pose Estimation and Beam Tracking for mmWave Vehicular Communication Systems
Simulation results demonstrate that the proposed SPEBT scheme is capable of providing precise pose estimation information and accurate beam tracking output, while reducing the proportion of beam training overhead to less than 5% averagely.
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks
Model-based, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques.
Active Simultaneously Transmitting and Reflecting (STAR)-RISs: Modelling and Analysis
The amplitude gains of the STAR element are derived for both coupled and independent phase-shift scenarios.
Simultaneously Transmitting and Reflecting (STAR) RIS Assisted Over-the-Air Computation Systems
The performance of over-the-air computation (AirComp) systems degrades due to the hostile channel conditions of wireless devices (WDs), which can be significantly improved by the employment of reconfigurable intelligent surfaces (RISs).
Exploiting STAR-RISs in Near-Field Communications
Exploiting the electric current distribution, a Green's function method based channel model is proposed.
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation
To solve this problem, we are the first to introduce an intermediate prototype for mining both deterministic category information from the support and adaptive category knowledge from the query.
Ranked #30 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Knowledge-aided Federated Learning for Energy-limited Wireless Networks
Inspired by this, we define a new objective function, i. e., the weighted scheduled data sample volume, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, bandwidth allocation, and power control.
Simultaneously Transmitting and Reflecting (STAR)-RISs: Are they Applicable to Dual-Sided Incidence?
A hardware model and a signal model are proposed for dual-sided simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), where the signal simultaneously incident on both sides of the surface.
DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networks
Moreover, in order to improve the performance of the MO-PPO algorithm, two update strategies, i. e., action-value-based update strategy (AVUS) and loss function-based update strategy (LFUS), are investigated.
Distributed Intelligence in Wireless Networks
In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native-AI wireless networks, with a focus on the basic concepts of native-AI wireless networks, on the AI-enabled edge computing, on the design of distributed learning architectures for heterogeneous networks, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications.
Joint Location and Beamforming Design for STAR-RIS Assisted NOMA Systems
Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) communication systems are investigated in its vicinity, where a STAR-RIS is deployed within a predefined region for establishing communication links for users.
WiFi-based Spatiotemporal Human Action Perception
WiFi-based sensing for human activity recognition (HAR) has recently become a hot topic as it brings great benefits when compared with video-based HAR, such as eliminating the demands of line-of-sight (LOS) and preserving privacy.
GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi
WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring due to the pervasive and unobtrusive nature of WiFi signals.
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition
Human Activity Recognition (HAR) has recently received remarkable attention in numerous applications such as assisted living and remote monitoring.
Learning Non-target Knowledge for Few-shot Semantic Segmentation
Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO-free target object segmentation result.
Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer
Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy.
Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications
The goal is to maximize the sum-rate of whole trajectories for the multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots, phase-shift coefficients of the RIS, and the power allocation of the AP, subject to predicted initial and final positions of robots and the quality of service (QoS) of each robot.
Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications
A multi-cell cluster-free NOMA framework is proposed, where both intra-cell and inter-cell interference are jointly mitigated via flexible cluster-free successive interference cancellation (SIC) and coordinated beamforming design.
Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach
Coverage and capacity are the important metrics for performance evaluation in wireless networks, while the coverage and capacity have several conflicting relationships, e. g. high transmit power contributes to large coverage but high inter-cell interference reduces the capacity performance.
A New Look at AI-Driven NOMA-F-RANs: Features Extraction, Cooperative Caching, and Cache-Aided Computing
Non-orthogonal multiple access (NOMA) enabled fog radio access networks (NOMA-F-RANs) have been taken as a promising enabler to release network congestion, reduce delivery latency, and improve fog user equipments' (F-UEs') quality of services (QoS).
Meta-learning for RIS-assisted NOMA Networks
Extensive simulation results demonstrate that the proposed QoS-based NOMA network achieves significantly higher transmission throughput compared to the conventional orthogonal multiple access (OMA) network.
Simultaneously Transmitting and Reflecting (STAR) Intelligent Omni-Surfaces, Their Modeling and Implementation
With the rapid development of advanced electromagnetic manipulation technologies, researchers and engineers are starting to study smart surfaces that can achieve enhanced coverages, high reconfigurability, and are easy to deploy.
Graph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networks
With the accelerated development of immersive applications and the explosive increment of internet-of-things (IoT) terminals, 6G would introduce terahertz (THz) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) technologies to meet the ultra-high-speed transmission and massive connectivity requirements.
Coverage Characterization of STAR-RIS Networks: NOMA and OMA
A sum coverage range maximization problem is formulated for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), where the resource allocation at the access point and the transmission and reflection coefficients at the STAR-RIS are jointly optimized to satisfy the communication requirements of users.
Multi-cell NOMA: Coherent Reconfigurable Intelligent Surfaces Model With Stochastic Geometry
Numerical results indicate that 1) although the interference from other cells is enhanced via the RISs, the performance of the RIS-aided user still enhances since the channel quality is strengthened more obviously; and 2) the SIC order can be altered by employing the RISs since the RISs improve the channel quality of the aided user.
Information Theory Information Theory
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks
To this end, the fundamentals of this framework are first introduced.
STAR-RISs: Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces
In this letter, simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are studied.
Joint User Activity and Data Detection in Grant-Free NOMA using Generative Neural Networks
Grant-free non-orthogonal multiple access (NOMA) is considered as one of the supporting technology for massive connectivity for future networks.
Integrated 3C in NOMA-enabled Remote-E-Health Systems
A novel framework is proposed to integrate communication, control and computing (3C) into the fifth-generation and beyond (5GB) wireless networks for satisfying the ultra-reliable low-latency connectivity requirements of remote-e-Health systems.
Deep Learning for Latent Events Forecasting in Twitter Aided Caching Networks
A novel Twitter context aided content caching (TAC) framework is proposed for enhancing the caching efficiency by taking advantage of the legibility and massive volume of Twitter data.
Reconfigurable Intelligent Surface-assisted Networks: Phase Alignment Categories
The reconfigurable intelligent surface (RIS) is one of the promising technology contributing to the next generation smart radio environment.
Robotic Communications for 5G and Beyond: Challenges and Research Opportunities
Furthermore, dynamic trajectory design and resource allocation for both indoor and outdoor robots are provided to verify the performance of robotic communications in the context of typical robotic application scenarios.
Robotics Systems and Control Signal Processing Systems and Control
Intelligent Reflecting Surface Aided Multi-Cell NOMA Networks
This paper proposes a novel framework of resource allocation in intelligent reflecting surface (IRS) aided multi-cell non-orthogonal multiple access (NOMA) networks, where a sum-rate maximization problem is formulated.
Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots
Our simulation results demonstrate that 1) With the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) The radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) The proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability.
Trajectory and Passive Beamforming Design for IRS-aided Multi-Robot NOMA Indoor Networks
A novel intelligent reflecting surface (IRS)-aided multi-robot network is proposed, where multiple mobile wheeled robots are served by an access point (AP) through non-orthogonal multiple access (NOMA).
Robotics
Reconfigurable Intelligent Surface Assisted Cooperative Non-orthogonal Multiple Access Systems
In each alternative procedure, the optimal solutions for the active beamforming vectors, transmit-relaying power and phase shifts are obtained.
Physical Layer Security of Intelligent Reflective Surface Aided NOMA Networks
We finally show that: 1) the expectation of the channel gain in the reflected links is determined both by the number of IRSs and by the Nakagami-m fading parameters; 2) The SOP of both receiver 1 and receiver 2 becomes unity, when the number of IRSs is sufficiently high; 3) The secrecy diversity orders are affected both by the number of IRSs and by the Nakagami-m fading parameters, whereas the high-SNR slopes are not affected by these parameters.
Non-Orthogonal Multiple Access (NOMA) With Multiple Intelligent Reflecting Surfaces
More importantly, simulation results reveal that a 3-bit resolution for discrete phase shifts is sufficient to achieve near-optimal outage performance.
Federated Learning in Multi-RIS Aided Systems
This paper investigates the problem of model aggregation in federated learning systems aided by multiple reconfigurable intelligent surfaces (RISs).
Information Theory Signal Processing Information Theory
NOMA in UAV-aided cellular offloading: A machine learning approach
A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network.
Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading
Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks
The energy consumption minimizing problem is formulated by jointly designing the movement of the UAV, phase shifts of the RIS, power allocation policy from the UAV to MUs, as well as determining the dynamic decoding order.
Intelligent Reflecting Surface Enhanced Indoor Robot Path Planning: A Radio Map based Approach
Based on the obtained channel power gain map, the communication-aware robot path planing problem is solved by exploiting graph theory.
Intelligent Reflecting Surface Enhanced Indoor Robot Path Planning Using Radio Maps
Based on the obtained channel power gain map, the communication-aware robot path planing problem is solved as a shortest path problem by exploiting graph theory.
Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks
In cache-enabling UAV NOMA networks, the caching placement of content caching phase and radio resource allocation of content delivery phase are crucial for network performance.
A Novel Physics-based Channel Model for Reconfigurable Intelligent Surface-assisted Multi-user Communication Systems
A novel physics-based RIS channel model is proposed.
Cache-enabling UAV Communications: Network Deployment and Resource Allocation
We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS).
Reconfigurable Intelligent Surfaces: Principles and Opportunities
In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies.
Resource Allocation for Multi-Cell IRS-Aided NOMA Networks
This paper proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service.
I/Q Imbalance Aware Nonlinear Wireless-Powered Relaying of B5G Networks: Security and Reliability Analysis
Physical layer security is known as a promising paradigm to ensure security for the beyond 5G (B5G) networks in the presence of eavesdroppers.
Downlink and Uplink Intelligent Reflecting Surface Aided Networks: NOMA and OMA
Intelligent reflecting surfaces (IRSs) are envisioned to provide reconfigurable wireless environments for future communication networks.
Performance Analysis of Intelligent Reflecting Surface Assisted NOMA Networks
Numerical results are presented to substantiate our analyses and demonstrate that: i) The outage behaviors of IRS-NOMA are superior to that of IRS-OMA and relaying schemes; ii) With increasing the number of reflecting elements, IRS-NOMA is capable of achieving enhanced outage performance; and iii) The M-th user has a larger ergodic rate compared to IRS-OMA and benchmarks.
Information Theory Information Theory
RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design
A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology.
Artificial Intelligence Aided Next-Generation Networks Relying on UAVs
Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments.
Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization
Our goal is to maximize the sum rate of all users by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, subject to successive interference cancellation decoding rate conditions and IRS reflecting elements constraints.
MIMO Assisted Networks Relying on Intelligent Reflective Surfaces
The network's SE and EE are also derived.
Information Theory Signal Processing Information Theory
