This paper proposes a learned-gating Mixture-of-Experts (MoE) framework that integrates multiple pretrained throughput prediction models without retraining for crossenvironment adaptation in commercial mobile networks. The framework retains global experts and cluster-specific experts trained in urban and suburban areas...
Mitsuki Nakamura, Motoharu Sasaki, Kenichi Kawamura et al.· IEEE International Symposium...· 0 citations
Efficient transmission of multimodal semantic features under strict physical-layer constraints is a key requirement for generative semantic communication. However, existing approaches often treat different semantic features uniformly despite their unequal impact on perceptual quality, which leads to inefficient resourc...
S. Kinoshita, Yue Yin, Mondher Bouazizi et al.· IEEE International Symposium...· 0 citations
The proliferation of Multi-Access Edge Computing (MEC) has led to massive data generation. This imposes complex and dynamic requirements on task scheduling. Conventional Uncrewed Aerial Vehicle (UAV) scheduling methods struggle with task diversity. They lack adaptability to stochastic environments. Large Language Model...
Xiong-Jie Zhou, Xin Guan, Hai-Yang Jiang et al.· IEEE Transactions on Mobile...· 0 citations
Intelligent reflecting surfaces (IRSs) have attracted extensive research interest for their passive beamforming gains. However, practical IRS-assisted systems rely heavily on accurate channel state information (CSI), which inevitably suffers from estimation errors. Motivated by this challenge, we first investigate the...
Shu-Cheng Yang, Yuwen Cao, Ji-Guang He et al.· IEEE Communications Letters· 1 citation
Specific Emitter Identification (SEI) is important for enhancing physical-layer security in resource-constrained Internet of Things (IoT) networks. However, Deep Learning (DL)-based SEI methods often incur high computational complexity and suffer performance degradation under noisy conditions. To address these issues,...
Yan Xiao, Lai Xu, Tian-Tian Tang et al.· IEEE Communications Letters· 0 citations
Unmanned aerial vehicle (UAV) recognition has become increasingly important for securing the low-altitude economy. Radio frequency (RF) signal analysis using deep learning (DL) has emerged as a promising approach to this task. However, training robust DL models relies on large-scale labeled datasets, which are costly t...
A cross-environment transfer learning framework for CSI-based HAR that integrates CSI preprocessing, adaptive amplitude-phase fusion via TinyGate, an R(2+1)D backbone, and two temporal modeling strategies, namely Bidirectional Long Short-Term Memory (Bi-LSTM) and Transformer is proposed.
Wi-Fi-based human activity recognition (HAR) using channel state information (CSI) provides a nonintrusive and device-free sensing solution for smart cities, healthcare monitoring, and smart homes. However, recognition performance often degrades when models trained on limited users are applied to unseen users due to va...
Fucheng Miao, Osamu Takyu, Shan Lin et al.· IEEE Internet of Things Jour...· 0 citations
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