Zhenkai Zhou
Collaborative Multi-point User Localization with Joint Communication and Sensing.
Rel. Carla Fabiana Chiasserini, Marco Palena. Politecnico di Torino, Master of science program in Communications Engineering, 2025
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (20MB) | Preview |
Abstract
Joint Communication and Sensing (JCS) is a key feature of 6G networks. Multi-point localization using Wi-Fi Channel State Information (CSI) is a prime example of JCS. However, deploying such applications at the network edge is complex. It requires fusing CSI data collected by multiple access points (APs) and managing significant computational workloads on resource-constrained devices. This creates a two-fold challenge: 1) At the application level, how much does collaboration (using multiple APs) actually improve performance, and how robust is this performance to sensor (AP) failure? 2) At the system level, how can we deploy these collaborative applications (which have different strategies, like model splitting or data fusion) in a dynamic and efficient way, instead of using static, manual configurations? This thesis addresses both challenges.
First, we implement and evaluate a complete indoor localization pipeline, we train this pipeline on a dataset of CSI traces collected from a multi-antenna, multi-anchor real-world scenario, creating two distinct versions: one for regression (predicting coordinates) and one for classification (predicting location labels)
Relators
Academic year
Publication type
Number of Pages
Course of studies
Classe di laurea
URI
![]() |
Modify record (reserved for operators) |
