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Found 17 papers, 1 papers with code
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.
From Single to Multi-Functional RIS: Architecture, Key Technologies, Challenges, and Applications
Although reconfigurable intelligent surfaces (RISs) have demonstrated the potential to boost network capacity and expand coverage by adjusting their electromagnetic properties, existing RIS architectures have certain limitations, such as double-fading attenuation and restricted half-space coverage.
Multi-Objective Optimization-Based Waveform Design for Multi-User and Multi-Target MIMO-ISAC Systems
In this paper, we investigate the waveform design problem in a downlink multi-user and multi-target ISAC system under different C&S performance preferences.
Multi-Objective Optimization-based Transmit Beamforming for Multi-Target and Multi-User MIMO-ISAC Systems
To achieve the Pareto boundary, the max-min system utility function method is employed, while considering the fairness between communication users and radar targets.
OFDM-Based Digital Semantic Communication with Importance Awareness
Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance.
Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework
To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL.
Performance Analysis and Optimization of Reconfigurable Multi-Functional Surface Assisted Wireless Communications
To realize a self-sustainable communication system, we investigate the use of MF-RIS in improving the sum-rate of multi-user wireless networks.
Multi-Functional Reconfigurable Intelligent Surface: System Modeling and Performance Optimization
In this paper, we propose and study a multi-functional reconfigurable intelligent surface (MF-RIS) architecture.
Explainable Semantic Communication for Text Tasks
In this paper, we propose a triplet-based explainable semantic communication (TESC) scheme for representing text semantics efficiently.
Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks
Implementing existing federated learning in massive Internet of Things (IoT) networks faces critical challenges such as imbalanced and statistically heterogeneous data and device diversity.
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning
Under perfect channel state information (CSI), the new framework minimizes the aggregated model's distortion and retains the local models' recoverability by optimizing the transmit beamformers of the devices, the receive beamformers of the BS, and the RIS configuration in an alternating manner.
Safeguarding NOMA Networks via Reconfigurable Dual-Functional Surface under Imperfect CSI
This paper investigates the use of the reconfigurable dual-functional surface to guarantee the full-space secure transmission in non-orthogonal multiple access (NOMA) networks.
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge
In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources.
Research on Resource Allocation for Efficient Federated Learning
In this framework, we minimize the weighted sum of system cost and learning cost by optimizing bandwidth, computing frequency, power allocation and subcarrier assignment.
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.
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
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.
