1 citations · 1 across the 3 of their papers we have counts for
7 papers
BitSemCom: A Bit-Level Semantic Communication Framework with Learnable Probabilistic Mapping
Haoshuo Zhang, Yufei Bo, Jianhua Mo +1
Most existing semantic communication systems based on joint source-channel coding (JSCC) employ analog modulation and are thus inherently incompatible with modern digital communica…
AirFM-DDA: Air-Interface Foundation Model in the Delay-Doppler-Angle Domain for AI-Native 6G
Kejia Bian, Meixia Tao, Jianhua Mo +2
The success of large foundation models is catalyzing a new paradigm for AI-native 6G network design: wireless foundation models for physical layer design. However, existing models…
SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming
Yeyue Cai, Jianhua Mo, Meixia Tao
Phase-time arrays, which integrate phase shifters (PSs) and true-time delays (TTDs), have emerged as a cost-effective architecture for generating frequency-dependent rainbow beams…
MARBLE-Net: Learning to Localize in Multipath Environment with Adaptive Rainbow Beams
Qiushi Liang, Yeyue Cai, Jianhua Mo +1
Integrated sensing and communication (ISAC) systems demand precise and efficient target localization, a task challenged by rich multipath propagation in complex wireless environmen…
CFARNet: Learning-Based High-Resolution Multi-Target Detection for Rainbow Beam Radar
Qiushi Liang, Yeyue Cai, Jianhua Mo +1
Millimeter-wave (mmWave) OFDM radar equipped with rainbow beamforming, enabled by phase-time arrays (PTAs), provides wide-angle coverage and is well-suited for fast real-time targe…
Fed-PELAD: Communication-Efficient Federated Learning for Massive MIMO CSI Feedback with Personalized Encoders and a LoRA-Adapted Shared Decoder
Yixiang Zhou, Tong Wu, Meixia Tao +1
This paper addresses the critical challenges of communication overhead, data heterogeneity, and privacy in deep learning for channel state information (CSI) feedback in massive MIM…