12 citations · 36 across the 28 of their papers we have counts for
29 papers
GLocFM: A Geometry-Aware Foundation Model for 3D Indoor Wireless Localization
Chenghong Bian, Chaozheng Wen, Hongze Chen +1
Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propaga…
Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds
Chaozheng Wen, Chenghong Bian, Hongze Chen +1
High-fidelity radio fields are typically simulated for every scene--transmitter configuration or fitted separately to each scene, failing to exploit propagation structures shared a…
Resolving Multi-Target Association in OFDM-based ISAC via Vision-aided Multi-Modal Learning
Meng Hua, Chenghong Bian, Deniz Gunduz
Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems commonly extract target parameters by peak-searching a delay-Doppler map…
GeoGS-CE: Learning Delay--Beam Channel Priors with 3D Gaussians for High-Mobility Scenarios
Yumeng Zhang, Jiajia Guo, Chaozheng Wen +2
Wideband channel estimation (CE) in high-mobility scenarios remains challenging because channel responses vary rapidly, while practical systems can allocate only sparse pilots to a…
P-WRFGS: Pruning 3D Gaussians for Efficient Wireless Radiance Field Construction
Chenghong Bian, Meng Hua, Deniz Gunduz
Wireless channel modeling is a key building block for next-generation wireless systems. Predicting the channel state information (CSI) across different transmitter locations can su…
Bridging Visual and Wireless Sensing via a Unified Radiation Field for 3D Radio Map Construction
Chaozheng Wen, Jingwen Tong, Zehong Lin +2
The emerging applications of next-generation wireless networks demand high-fidelity environmental intelligence. 3D radio maps bridge physical environments and electromagnetic propa…
In-Context Learning for Deep Joint Source-Channel Coding Over MIMO Channels
Meng Hua, Wenjing Zhang, Chenghong Bian +1
Large language models have demonstrated the ability to perform \textit{in-context learning} (ICL), whereby the model performs predictions by directly mapping the query and a few ex…
Implementing Neural Networks Over-the-Air via Reconfigurable Intelligent Surfaces
Meng Hua, Chenghong Bian, Haotian Wu +1
In this paper, we investigate reconfigurable intelligent surface (RIS)-aided multiple-input-multiple-output (MIMO) OAC systems designed to emulate the fully-connected (FC) layer of…
Towards AI-Native Fronthaul: Neural Compression for NextG Cloud RAN
Chenghong Bian, Yulin Shao, Deniz Gunduz
The rapid growth of data traffic and the emerging AI-native wireless architectures in NextG cellular systems place new demands on the fronthaul links of Cloud Radio Access Networks…
Realizing Fully-Connected Layers Over the Air via Reconfigurable Intelligent Surfaces
Meng Hua, Chenghong Bian, Haotian Wu +1
By leveraging the waveform superposition property of the multiple access channel, over-the-air computation (AirComp) enables the execution of digital computations through analog me…
Over-the-Air Inference over Multi-hop MIMO Networks
Chenghong Bian, Meng Hua, Deniz Gunduz
A novel over-the-air machine learning framework over multi-hop multiple-input and multiple-output (MIMO) networks is proposed. The core idea is to imitate fully connected (FC) neur…
CRC-Assisted Channel Codes for Integrated Passive Sensing and Communications
Chenghong Bian, Kaitao Meng, Huihui Wu +2
We propose a novel coded integrated passive sensing and communication (CIPSAC) system with orthogonal frequency division multiplexing (OFDM), where a multi-antenna base station (BS…
LISAC: Learned Coded Waveform Design for ISAC with OFDM
Chenghong Bian, Yumeng Zhang, Meng Hua +2
We propose deep learning based coded waveform design for integrated sensing and communication (ISAC) with orthogonal frequency-division multiplexing (OFDM). Our goal is to design a…
A Deep Joint Source-Channel Coding Scheme for Hybrid Mobile Multi-hop Networks
Chenghong Bian, Yulin Shao, Deniz Gündüz
Efficient data transmission across mobile multi-hop networks that connect edge devices to core servers presents significant challenges, particularly due to the variability in link…
Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks
Chenghong Bian, Yulin Shao, Haotian Wu +2
We introduce deep joint source-channel coding (DeepJSCC) schemes for image transmission over cooperative relay channels. The relay either amplifies-and-forwards its received signal…
Point Cloud in the Air
Yulin Shao, Chenghong Bian, Li Yang +3
Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and…
A Hybrid Joint Source-Channel Coding Scheme for Mobile Multi-hop Networks
Chenghong Bian, Yulin Shao, Deniz Gunduz
We propose a novel hybrid joint source-channel coding (JSCC) scheme for robust image transmission over multi-hop networks. In the considered scenario, a mobile user wants to delive…
Deep Joint Source-Channel Coding for Adaptive Image Transmission over MIMO Channels
Haotian Wu, Yulin Shao, Chenghong Bian +2
This paper introduces a vision transformer (ViT)-based deep joint source and channel coding (DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output (M…
Over-the-Air Learning-based Geometry Point Cloud Transmission
Chenghong Bian, Yulin Shao, Deniz Gunduz
This paper presents novel solutions for the efficient and reliable transmission of point clouds over wireless channels for real-time applications. We first propose SEmatic Point cl…
Do not Interfere but Cooperate: A Fully Learnable Code Design for Multi-Access Channels with Feedback
Emre Ozfatura, Chenghong Bian, Deniz Gunduz
Data-driven deep learning based code designs, including low-complexity neural decoders for existing codes, or end-to-end trainable auto-encoders have exhibited impressive results,…
DeepJSCC-l++: Robust and Bandwidth-Adaptive Wireless Image Transmission
Chenghong Bian, Yulin Shao, Deniz Gunduz
This paper presents a novel vision transformer (ViT) based deep joint source channel coding (DeepJSCC) scheme, dubbed DeepJSCC-l++, which can be adaptive to multiple target bandwid…
Deep Joint Source-Channel Coding Over Cooperative Relay Networks
Chenghong Bian, Yulin Shao, Haotian Wu +1
This paper presents a novel deep joint source-channel coding (DeepJSCC) scheme for image transmission over a half-duplex cooperative relay channel. Specifically, we apply DeepJSCC…
Vision Transformer for Adaptive Image Transmission over MIMO Channels
Haotian Wu, Yulin Shao, Chenghong Bian +2
This paper presents a vision transformer (ViT) based joint source and channel coding (JSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) system…
Space-time design for deep joint source channel coding of images Over MIMO channels
Chenghong Bian, Yulin Shao, Haotian Wu +1
We propose novel deep joint source-channel coding (DeepJSCC) algorithms for wireless image transmission over multi-input multi-output (MIMO) Rayleigh fading channels, when channel…