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Found 45 papers, 4 papers with code
Redefining Information Freshness: AoGI for Generative AI in 6G Networks
Generative Artificial Intelligence (GenAI) is playing an increasingly important role in enriching and facilitating human life by generating various useful information, of which real-time GenAI is a significant part and has great potential in applications such as real-time robot control, automated driving, augmented reality, etc.
Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based?
To solve this highly coupled and nonconvex problem, both optimization-based and learning-based methods are proposed.
GPASS: Deep Learning for Beamforming in Pinching-Antenna Systems (PASS)
The GPASS is with a staged architecture, where the positions of pinching antennas are first learned by a sub-GNN.
Diffusion Model for Multiple Antenna Communications
The potential of applying diffusion models (DMs) for multiple antenna communications is discussed.
Vehicular Multi-Tier Distributed Computing with Hybrid THz-RF Transmission in Satellite-Terrestrial Integrated Networks
We formulate a non-convex optimization problem aimed at maximizing computation efficiency by jointly optimizing bandwidth allocation, task allocation, subchannel-vehicle matching and power allocation.
When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective
In this article, we develop a NOMA-assisted uplink xURLLC network architecture that incorporates an SNC-based SQP theoretical framework (SNC-SQP) to support tail distribution analysis in terms of delay, age-of-information (AoI), and reliability.
Cramér-Rao Bound Optimization for Near-Field Sensing with Continuous-Aperture Arrays
A Cram\'er-Rao bound (CRB) optimization framework for near-field sensing (NISE) with continuous-aperture arrays (CAPAs) is proposed.
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.
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.
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.
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.
Federated Contrastive Learning for Personalized Semantic Communication
Simulation results verify the superiority of the proposed FedCL framework compared to other distributed learning benchmarks in terms of task performance and robustness under different numbers of clients and channel conditions, especially in low signal-to-noise ratio and highly heterogeneous data scenarios.
Intelligent Resource Allocation for UAV-Based Cognitive NOMA Networks: An Active Inference Approach
This paper investigates the design of joint subchannel and power allocation in an uplink UAV-based cognitive NOMA network.
Active Inference for Sum Rate Maximization in UAV-Assisted Cognitive NOMA Networks
Given the surge in wireless data traffic driven by the emerging Internet of Things (IoT), unmanned aerial vehicles (UAVs), cognitive radio (CR), and non-orthogonal multiple access (NOMA) have been recognized as promising techniques to overcome massive connectivity issues.
Adaptive Federated Pruning in Hierarchical Wireless Networks
We present the convergence analysis of an upper on the l2 norm of gradients for HFL with model pruning, analyze the computation and communication latency of the proposed model pruning scheme, and formulate an optimization problem to maximize the convergence rate under a given latency threshold by jointly optimizing the pruning ratio and wireless resource allocation.
Computation and Privacy Protection for Satellite-Ground Digital Twin Networks
Satellite-ground integrated digital twin networks (SGIDTNs) are regarded as innovative network architectures for reducing network congestion, enabling nearly-instant data mapping from the physical world to digital systems, and offering ubiquitous intelligence services to terrestrial users.
Federated Learning and Meta Learning: Approaches, Applications, and Directions
Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
Goal-Oriented Semantic Communications for 6G Networks
In this article, we propose a generic goal-oriented semantic communication framework for various tasks with diverse data types, which incorporates both semantic level information and effectiveness-aware performance metrics.
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.
Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing
To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution.
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach
This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and a cognitive-UAV is employed as a case study.
Efficient Wireless Federated Learning with Partial Model Aggregation
On this basis, we maximize the scheduled data size to minimize the global loss function through jointly optimize the device scheduling, bandwidth allocation, computation and communication time division policies with the assistance of Lyapunov optimization.
Towards Explainable Meta-Learning for DDoS Detection
With the ever increasing of new intrusions, intrusion detection task rely on Artificial Intelligence more and more.
Optimization of Grant-Free NOMA with Multiple Configured-Grants for mURLLC
To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs.
Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT
In this paper, we investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks.
AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning
Numerical results illustrate that the DRL-based scheme is able to track the changes in the environment, learn the near-optimal pilot assignment, and achieve a close performance to that of the optimum pilot assignment performed by exhaustive search, while maintaining a low computational complexity.
Machine Learning for Massive Industrial Internet of Things
With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements.
Learning based signal detection for MIMO systems with unknown noise statistics
Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics.
Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems
This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems.
RACH in Self-Powered NB-IoT Networks: Energy Availability and Performance Evaluation
In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission.
Analysis of Random Access in NB-IoT Networks with Three Coverage Enhancement Groups: A Stochastic Geometry Approach
NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks.
Low-Complexity Robust Beamforming Design for IRS-Aided MISO Systems with Imperfect Channels
In this paper, large-scale intelligent reflecting sur-face (IRS)-assisted multiple-input single-output (MISO) system is considered in the presence of channel uncertainty.
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.
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).
Joint Transmit Power and Placement Optimization for URLLC-enabled UAV Relay Systems
This letter considers an unmanned aerial vehicle (UAV)-enabled relay communication system for delivering latency-critical messages with ultra-high reliability, where the relay is operating under amplifier-and-forward (AF) mode.
Robust Transmission Design for Intelligent Reflecting Surface Aided Secure Communication Systems with Imperfect Cascaded CSI
To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model that usually models the channel estimation error.
Analyzing Grant-Free Access for URLLC Service
Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances.
Artificial-Noise-Aided Secure MIMO Wireless Communications via Intelligent Reflecting Surface
To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the TPC matrix, AN covariance matrix, and phase shifts while keeping SR non-decreasing.
A Framework of Robust Transmission Design for IRS-aided MISO Communications with Imperfect Cascaded Channels
Specifically, the transmit power minimization problems are formulated subject to the worst-case rate constraints under the bounded CSI error model and the rate outage probability constraints under the statistical CSI error model, respectively.
Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems
In this paper, we study the worst-case robust beamforming design for an IRS-aided multiuser multiple-input single-output (MU-MISO) system under the assumption of imperfect CSI.
Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing
In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE).
Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing
In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS.
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge
To scale learning across geographic areas, a spatial transfer learning scheme is proposed to further promote the learning efficiency of distributed DRL entities by exploiting the traffic demand correlations.
Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems
We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint.
A Machine Learning Approach for Power Allocation in HetNets Considering QoS
However, as the density of the network increases so does the complexity of such resource allocation methods.
Information Theory Information Theory
