12 citations · 36 across the 28 of their papers we have counts for
5 papers · 1 filter
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…