Loss Design for Single-carrier Joint Communication and Neural Network-based Sensing
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910354271895552 |
|---|---|
| author | Muth, Charlotte Geiger, Benedikt Gaviria, Daniel Gil Schmalen, Laurent |
| author_facet | Muth, Charlotte Geiger, Benedikt Gaviria, Daniel Gil Schmalen, Laurent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02929 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Loss Design for Single-carrier Joint Communication and Neural Network-based Sensing Muth, Charlotte Geiger, Benedikt Gaviria, Daniel Gil Schmalen, Laurent Signal Processing 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. |
| title | Loss Design for Single-carrier Joint Communication and Neural Network-based Sensing |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2403.02929 |