Windowing Optimization for Fingerprint-Spectrum-Based Passive Sensing in Perceptive Mobile Networks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917766349455360 |
|---|---|
| author | Wang, Xiao-Yang Yang, Shaoshi Zhai, Hou-Yu Masouros, Christos Zhang, J. Andrew |
| author_facet | Wang, Xiao-Yang Yang, Shaoshi Zhai, Hou-Yu Masouros, Christos Zhang, J. Andrew |
| contents | Perceptive mobile networks (PMN) have been widely recognized as a pivotal pillar for the sixth generation (6G) mobile communication systems. However, the asynchronicity between transmitters and receivers results in velocity and range ambiguity, which seriously degrades the sensing performance. To mitigate the ambiguity, carrier frequency offset (CFO) and time offset (TO) synchronizations have been studied in the literature. However, their performance can be significantly affected by the specific choice of the window functions harnessed. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We firstly derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then test a practical "window function" by utilizing the multiple signal classification (MUSIC) algorithm, which may lead to excellent synchronization performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00950 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Windowing Optimization for Fingerprint-Spectrum-Based Passive Sensing in Perceptive Mobile Networks Wang, Xiao-Yang Yang, Shaoshi Zhai, Hou-Yu Masouros, Christos Zhang, J. Andrew Signal Processing Networking and Internet Architecture Perceptive mobile networks (PMN) have been widely recognized as a pivotal pillar for the sixth generation (6G) mobile communication systems. However, the asynchronicity between transmitters and receivers results in velocity and range ambiguity, which seriously degrades the sensing performance. To mitigate the ambiguity, carrier frequency offset (CFO) and time offset (TO) synchronizations have been studied in the literature. However, their performance can be significantly affected by the specific choice of the window functions harnessed. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We firstly derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then test a practical "window function" by utilizing the multiple signal classification (MUSIC) algorithm, which may lead to excellent synchronization performance. |
| title | Windowing Optimization for Fingerprint-Spectrum-Based Passive Sensing in Perceptive Mobile Networks |
| topic | Signal Processing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2409.00950 |