4 citations · 5 across the 27 of their papers we have counts for
29 papers
WiFo-INR: A Wireless Foundation Model Based on Implicit Neural Representations
Boxun Liu, Xuanyu Liu, Shijian Gao +2
Wireless foundation models are emerging as a promising paradigm for AI-native physical-layer design. However, existing methods typically model channel state information (CSI) as im…
WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM)
Xuanyu Liu, Shijian Gao, Boxun Liu +2
Current learning-based wireless methods struggle with generalization due to the fragmented processing of communication and sensing data. WiFo-MiSAC addresses this as a task-agnosti…
FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking
Shijian Gao, Jiahui Liang, Yifeng Yuan +3
Precise aerial radio environment characterization is vital for low-altitude airspace planning. However, existing datasets and construction methods lack the high-resolution granular…
Multi-Tier UAV Edge Computing Towards Long-Term Energy Stability for Low Altitude Networks
Yufei Ye, Shijian Gao, Xinhu Zheng +1
The agile mobility of Unmanned Aerial Vehicles (UAVs) makes them ideal for low-altitude edge computing. This paper proposes a novel multi-tier UAV edge computing system where light…
WiFo-E: A Scalable Wireless Foundation Model for End-to-End FDD Precoding in Communication Networks
Weibo Wen, Shijian Gao, Haotian Zhang +2
Accurate precoding in massive multiple-input multiple-output (MIMO) frequency-division duplexing (FDD) systems relies on efficient channel state information (CSI) acquisition. End-…
WiFo-MUD: Wireless Foundation Model for Heterogeneous Multi-User Demodulator
Zonghui Yang, Shijian Gao, Xuesong Cai +2
Multi-user signal demodulation is critical to wireless communications, directly impacting transmission reliability and efficiency. However, existing demodulators underperform in ge…
WiFo-M: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model
Haotian Zhang, Shijian Gao, Xiang Cheng
The emerging convergence of next-generation wireless networks and agentic artificial intelligence (AI) is inspiring a new vision: embodied intelligent network entities utilize envi…
WiFo-2: a generalist foundation model unifies heterogeneous wireless system design
Boxun Liu, Xuanyu Liu, Shijian Gao +3
Emerging sixth-generation wireless systems are increasingly heterogeneous, with compatibility across diverse configurations, ubiquitous coverage, and expanded functionalities. Alth…
Tiny-WiFo: A Lightweight Wireless Foundation Model for Channel Prediction via Multi-Component Adaptive Knowledge Distillation
Haotian Zhang, Shijian Gao, Xiang Cheng
The massive scale of Wireless Foundation Models (FMs) hinders their real-time deployment on edge devices. This letter moves beyond standard knowledge distillation by introducing a…
Multi-Tier UAV Edge Computing for Low Altitude Networks Towards Long-Term Energy Stability
Yufei Ye, Shijian Gao, Xinhu Zheng +1
This paper presents a novel multi-tier UAV-assisted edge computing system designed for low-altitude networks. The system comprises vehicle users, lightweight Low-Tier UAVs (L-UAVs)…
WiFo-CF: Wireless Foundation Model for CSI Feedback
Xuanyu Liu, Shijian Gao, Boxun Liu +2
Deep learning-based channel state information (CSI) feedback schemes demonstrate strong compression capabilities but are typically constrained to fixed system configurations, limit…
Synesthesia of Machines (SoM)-Enhanced Sub-THz ISAC Transmission for Air-Ground Network
Zonghui Yang, Shijian Gao, Xiang Cheng +1
Integrated sensing and communication (ISAC) within sub-THz frequencies is crucial for future air-ground networks, but unique propagation characteristics and hardware limitations pr…
Synesthesia of Machines (SoM)-Aided Online FDD Precoding via Heterogeneous Multi-Modal Sensing: A Vertical Federated Learning Approach
Haotian Zhang, Shijian Gao, Weibo Wen +2
This paper investigates a heterogeneous multi-vehicle, multi-modal sensing (H-MVMM) aided online precoding problem. The proposed H-MVMM scheme utilizes a vertical federated learnin…
Sequential Task Assignment and Resource Allocation in V2X-Enabled Mobile Edge Computing
Yufei Ye, Shijian Gao, Xinhu Zheng +1
Nowadays, the convergence of mobile edge computing (MEC) and vehicular networks has emerged as a vital enabler for the ever-increasing intelligent onboard applications. This paper…
LLM4WM: Adapting LLM for Wireless Multi-Tasking
Xuanyu Liu, Shijian Gao, Boxun Liu +2
The wireless channel is fundamental to communication, encompassing numerous tasks collectively referred to as channel-associated tasks. These tasks can leverage joint learning base…
Synesthesia of Machines (SoM)-Aided FDD Precoding with Sensing Heterogeneity: A Vertical Federated Learning Approach
Haotian Zhang, Shijian Gao, Weibo Wen +1
High complexity in precoding design for frequency division duplex systems necessitates streamlined solutions. Guided by Synesthesia of Machines (SoM), this paper introduces a heter…
Predictive Target-to-User Association in Complex Scenarios via Hybrid-Field ISAC Signaling
Yifeng Yuan, Miaowen Wen, Xinhu Zheng +2
This paper presents a novel and robust target-to-user (T2U) association framework to support reliable vehicle-to-infrastructure (V2I) networks that potentially operate within the h…
Synesthesia of Machine (SoM)-Driven Analog Precoder Optimization for Enhanced ISAC Performance in Sub-THz Systems
Zonghui Yang, Shijian Gao, Xiang Cheng
Integrated sensing and communication (ISAC) is anticipated to be widely used in future sub-terahertz (sub-THz) systems. With the line-of-sight (LoS) propagation characteristics of…
WiFo: Wireless Foundation Model for Channel Prediction
Boxun Liu, Shijian Gao, Xuanyu Liu +2
Channel prediction permits to acquire channel state information (CSI) without signaling overhead. However, almost all existing channel prediction methods necessitate the deployment…
Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics
Zonghui Yang, Shijian Gao, Xiang Cheng +1
Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significan…
Synesthesia of Machines (SoM)-Enhanced Wideband Multi-User CSI Learning With LiDAR Sensing
Haotian Zhang, Shijian Gao, Xiang Cheng +1
Light detection and ranging (LiDAR) has been utilized for optimizing wireless communications due to its ability to detect the environment. This paper explores the use of LiDAR in c…
LLM4CP: Adapting Large Language Models for Channel Prediction
Boxun Liu, Xuanyu Liu, Shijian Gao +2
Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel predict…
Doubly-Dynamic ISAC Precoding for Vehicular Networks: A Constrained Deep Reinforcement Learning (CDRL) Approach
Zonghui Yang, Shijian Gao, Xiang Cheng
Integrated sensing and communication (ISAC) technology is essential for supporting vehicular networks. However, the communication channel in this scenario exhibits time variations,…
Beam Pattern Modulation Embedded Hybrid Transceiver Optimization for Integrated Sensing and Communication
Boxun Liu, Shijian Gao, Zonghui Yang +2
Integrated sensing and communication (ISAC) emerges as a promising technology for B5G/6G, particularly in the millimeter-wave (mmWave) band. However, the widely utilized hybrid arc…