7 papers
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…
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…
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…
Variable-Length Feedback Codes via Deep Learning
Wenwei Lai, Yulin Shao, Yu Ding +1
Variable-length feedback coding has the potential to significantly enhance communication reliability in finite block length scenarios by adapting coding strategies based on real-ti…
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…
Energy-Aware Dynamic Neural Inference
Marcello Bullo, Seifallah Jardak, Pietro Carnelli +1
The growing demand for intelligent applications beyond the network edge, coupled with the need for sustainable operation, are driving the seamless integration of deep learning (DL)…