Abstract
Two-dimensional (2-D) field reconstruction presents a challenge in opportunistic Integrated Sensing and Communication (ISAC), where weather phenomena, such as rain fields or humidity, are sensed and mapped through their effect on wireless communication. In this paper, we analyze the inherent limitations in reconstruction accuracy by deriving the Bayesian Cramér-Rao Bound (BCRB) for field mapping modeled by 2-D B-splines for two sensor types: line (e.g., commercial microwave links) and point (e.g., rain gauges) sensors. In particular, we utilize this bound to optimize the allocation of additional designated point sensors within an existing, given opportunistic sensor network, where sensors are randomly located.
| Original language | English |
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| Title of host publication | 2025 IEEE Statistical Signal Processing Workshop, SSP 2025 |
| Publisher | IEEE Computer Society |
| Pages | 131-135 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798331518004 |
| DOIs | |
| State | Published - 2025 |
| Externally published | Yes |
| Event | 2025 IEEE Statistical Signal Processing Workshop, SSP 2025 - Edinburgh, United Kingdom Duration: 8 Jun 2025 → 11 Jun 2025 |
Publication series
| Name | IEEE Workshop on Statistical Signal Processing Proceedings |
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| ISSN (Print) | 2373-0803 |
| ISSN (Electronic) | 2693-3551 |
Conference
| Conference | 2025 IEEE Statistical Signal Processing Workshop, SSP 2025 |
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| Country/Territory | United Kingdom |
| City | Edinburgh |
| Period | 8/06/25 → 11/06/25 |
Bibliographical note
Publisher Copyright:© 2025 IEEE.
Keywords
- B-Spline
- Bayesian Cramér-Rao Bound
- CML
- ISAC
- Rain