Maximizing Achievable Covert Rate in XL-RIS Empowered Near-Field Communications
This paper proposes an optimization framework to maximize the achievable covert rate in an XL-RIS empowered near-field communication system, by jointly optimizing the hybrid analog-digital beamforming at the transmitter and the reflection coefficient matrix at the XL-RIS.
RIS-Aided Receive Generalized Spatial Modulation with Reflecting Modulation
The proposed RIS-aided receive generalized spatial modulation (RIS-RGSM) scheme combines generalized spatial modulation (GSM) with reconfigurable intelligent surface (RIS) to enhance spectral efficiency and transmission performance under limited antenna configurations.
非常に大きな遅延広がりを持つチャネルにおけるOTFSチャネル推定と検出
大きな遅延広がりを持つチャネルにおいて、従来のチャネル推定手法では推定が失敗するため、2段階のチャネル推定手法を提案し、低複雑度のMRC検出アルゴリズムを修正することで、OTFSシステムの性能を改善する。
Deep Learning-based Compression and Disentangled Representation of Dual-Polarized Massive MIMO Channel State Information
The proposed DiReNet network effectively disentangles dual-polarized channel state information (CSI) into polarization-shared and polarization-specific representations, enabling efficient compression and recovery of CSI while reducing information redundancy.
Challenges and Strategies for Wireless Physical-Layer Foundation Model Development
Evolution from task-specific to adaptable AI models in wireless networks is crucial for future advancements.
Physics-Inspired Deep Learning Anti-Aliasing Framework for Efficient Channel State Feedback
Utilizing physics-inspired deep learning, the proposed framework addresses aliasing effects in CSI feedback through innovative upsampling techniques.
Optimizing User Connection and Resource Allocation in Blockchain-Empowered Metaverse over 6G Wireless Communications
The author focuses on optimizing user connectivity and resource allocation in a blockchain-enabled Metaverse, introducing the trust-cost ratio (TCR) to ensure sustained user engagement and trust.
Efficient User Association and Resource Allocation in Large Language Model Based Mobile Edge Computing System over Wireless Communications
The authors propose a collaborative framework for model training to optimize performance by sharing user data and model adapters between users and servers. The DASHF algorithm is central to their methodology, enabling efficient resource allocation and training.
Probabilistic Semantic Communication with Rate Splitting in Wireless Networks
The author explores joint transmission and computation resource allocation for probabilistic semantic communication with rate splitting in wireless networks, utilizing shared probability graphs to compress data efficiently.
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks
The author proposes a novel multi-agent reinforcement learning framework, CARLTON, to optimize channel allocation in cognitive interference networks. By utilizing a deep reinforcement learning approach, the algorithm demonstrates exceptional performance and robust generalization compared to existing methods.