2024/03/05 by Charlotte Muth, Muth, Charlotte, B. Geiger +5 · 2 citations
Engineering · #Advanced Photonic Communication Systems #Antenna Design and Optimization #Artificial intelligence #Artificial neural network #Computer network #Computer science #Engineering #FOS: Electrical engineering #Joint (building) #Optical Systems and Laser Technology #Signal Processing (eess.SP) #Structural engineering #Telecommunications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2403.02929
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/03/05 · openalex created_date 2024/03/07 · openalex updated_date 2026/07/28
We evaluate the influence of multi-snapshot sensing and varying signal-to-noise ratio (SNR) on the overall performance of neural network (NN)-based joint communication and sensing (JCAS) systems. To enhance the training behavior, we decouple the loss functions from the respective SNR values and the number of sensing snapshots, using bounds of the sensing performance. Pre-processing is done through conventional sensing signal processing steps on the inputs to the sensing NN. The proposed method outperforms classical algorithms, such as a Neyman-Pearson-based power detector for object detection and ESPRIT for angle of arrival (AoA) estimation for quadrature amplitude modulation (QAM) at low SNRs.