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Found 40 papers, 1 papers with code
Personalized Wireless Federated Learning for Large Language Models
Large Language Models (LLMs) have revolutionized natural language processing tasks.
Large Generative Model Assisted 3D Semantic Communication
Semantic Communication (SC) is a novel paradigm for data transmission in 6G.
Large Language Model Enhanced Multi-Agent Systems for 6G Communications
The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e. g., network optimization and management by allowing users to input task requirements to LLMs by nature language.
Large AI Model Empowered Multimodal Semantic Communications
To this end, we propose a Large AI Model-based Multimodal SC (LAM-MSC) framework, in which we first present the MLM-based Multimodal Alignment (MMA) that utilizes the MLM to enable the transformation between multimodal and unimodal data while preserving semantic consistency.
LAMBO: Large Language Model Empowered Edge Intelligence
Next-generation edge intelligence is anticipated to bring huge benefits to various applications, e. g., offloading systems.
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning
To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy.
Large AI Model-Based Semantic Communications
Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed-reality, and the Internet of everything.
Over-the-Air Federated Averaging with Limited Power and Privacy Budgets
We aim to improve the learning performance by jointly designing the device scheduling, alignment coefficient, and the number of aggregation rounds of federated averaging (FedAvg) subject to sum power and privacy constraints.
Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control
In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.
Device Scheduling for Over-the-Air Federated Learning with Differential Privacy
The scheme schedules the devices with better channel conditions in the training to avoid the problem that the alignment coefficient is limited by the device with the worst channel condition in the system.
Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System
To address this issue, we propose a joint optimization framework of deployment and trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV.
Toward Secure and Private Over-the-Air Federated Learning
In this paper, a novel secure and private over-the-air federated learning (SP-OTA-FL) framework is studied where noise is employed to protect data privacy and system security.
Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities
In this paper, to reduce the congestion rate at the city center and increase the quality of experience (QoE) of each user, the framework of long-range autonomous valet parking (LAVP) is presented, where an Autonomous Vehicle (AV) is deployed in the city, which can pick up, drop off users at their required spots, and then drive to the car park out of city center autonomously.
Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Model
At the SP, The MoG model estimates the noise added to perturbed ratings and the MF algorithm predicts missing ratings.
Self-Sustainable Reconfigurable Intelligent Surface Aided Simultaneous Terahertz Information and Power Transfer (STIPT)
This paper proposes a new simultaneous terahertz (THz) information and power transfer (STIPT) system, which is assisted by reconfigurable intelligent surface (RIS) for both the information data and power transmission.
Secure Wireless Communication in RIS-Aided MISO Systems with Hardware Impairments
In this paper, we study the robust transmission design for a reconfigurable intelligent surface (RIS)-aided secure communication system in the presence of transceiver hardware impairments.
Information Theory Signal Processing Information Theory
Outage Constrained Robust Transmission Design for IRS-aided Secure Communications with Direct Communication Links
In this paper, we investigate the robust outage constrained transmission design for an intelligent reflecting surface (IRS) aided secure communication system.
Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions
Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), are regarded as one of the most promising and revolutionizing techniques for enhancing the spectrum and/or energy efficiency of wireless systems.
UAV-Assisted and Intelligent Reflecting Surfaces-Supported Terahertz Communications
In this paper, unmanned aerial vehicles (UAVs) and intelligent reflective surface (IRS) are utilized to support terahertz (THz) communications.
Power Scaling Law Analysis and Phase Shift Optimization of RIS-aided Massive MIMO Systems with Statistical CSI
We consider the Rician channel model and exploit the long-time statistical CSI to design the phase shifts of the RIS, while the maximum ratio combination (MRC) technique is applied for the active beamforming at the base station (BS) relying on the instantaneous CSI.
Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks
Federated learning (FL) in a bandwidth-limited network with energy-limited user equipments (UEs) is under-explored.
Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing
An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground.
Stochastic Learning-Based Robust Beamforming Design for RIS-Aided Millimeter-Wave Systems in the Presence of Random Blockages
To enhance the robustness of hybrid analog-digital beamforming in the presence of random blockages, we formulate a stochastic optimization problem based on the minimization of the sum outage probability.
Uplink Achievable Rate of Intelligent Reflecting Surface-Aided Millimeter-Wave Communications with Low-Resolution ADC and Phase Noise
In this paper, we derive the uplink achievable rate expression of intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) systems, taking into account the phase noise at IRS and the quantization error at base stations (BSs).
A Review on Computational Intelligence Techniques in Cloud and Edge Computing
These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions.
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.
Joint Trajectory and Passive Beamforming Design for Intelligent Reflecting Surface-Aided UAV Communications: A Deep Reinforcement Learning Approach
In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) communication system is studied, where the UAV is deployed to serve the user equipment (UE) with the assistance of multiple IRSs mounted on several buildings to enhance the communication quality between UAV and UE.
User Cooperation for IRS-aided Secure SWIPT MIMO Systems
In this paper, intelligent reflecting surface (IRS) is proposed to enhance the physical layer security in the Rician fading channel where the angular direction of the eavesdropper is aligned with a legitimate user.
Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Acceleration
We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system.
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.
Robust Beamforming Design for Intelligent Reflecting Surface Aided Cognitive Radio Systems with Imperfect Cascaded CSI
Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's minimum transmit power and feasibility rate of the optimization problems.
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.
AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment -- Challenges and Solutions
By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation networks.
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, transmission power and resource allocation in the large-scale mobile edge computing (MEC) system.
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).
Bit-level Optimized Neural Network for Multi-antenna Channel Quantization
Quantized channel state information (CSI) plays a critical role in precoding design which helps reap the merits of multiple-input multiple-output (MIMO) technology.
Information Theory Signal Processing Information Theory
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.
RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC
In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC), i. e., UAVE is studied, where several UAVs are deployed as flying MEC platform to provide computing resource to ground user equipments (UEs).
