12 papers
Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions
Meng Hua, Itsik Bergel, Deniz Gündüz
Wireless physical neural networks (WPNNs) embed neural computation directly into analog hardware, offering lower energy consumption and latency than conventional digital implementa…
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…
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…
Realization of a Fully Connected Neural Layer Over-the-Air through Multi-hop Amplify-and-Forward Relays
Tolga Girici, Meng Hua, Deniz Gündüz
We study the problem of implementing a fully-connected layer of a neural network using wireless over-the-air computing. We assume a multi hop system with a multi-antenna transmitte…
Pilot Allocation for Multi-Hop Over-the-Air Neural Inference under Imperfect CSI
Tolga Girici, Meng Hua, Deniz Gündüz
A multi-hop amplify-and-forward (AF) relay network can emulate a fully connected (FC) neural network layer via over-the-air (OTA) computation. However, achieving high emulation acc…
Relay-Assisted Activation-Integrated SIM for Wireless Physical Neural Networks
Meng Hua, Deniz Gündüz
Wireless physical neural networks (WPNNs) have emerged as a promising paradigm for performing neural computation directly in the physical layer of wireless systems, offering low la…
Wireless Physical Neural Networks (WPNNs): Opportunities and Challenges
Meng Hua, Itsik Bergel, Tolga Girici +2
Wireless communication systems exhibit structural and functional similarities to neural networks: signals propagate through cascaded elements, interact with the environment, and un…
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…
CNNs in the Air via Reconfigurable Intelligent Surfaces
Meng Hua, Haotian Wu, Deniz Gündüz
This paper introduces AirCNN, a novel paradigm for implementing convolutional neural networks (CNNs) via over-the-air (OTA) analog computation. By leveraging multiple reconfigurabl…
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…
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…