Abstract
In this paper, a machine learning (ML)-based channel allocation algorithm is proposed to form a secure communication zone in indoor visible light communication (VLC) systems. The algorithm first employs the probabilistic neural network (PNN), which classifies the VLC transmitter (Tx) based on its proximity to the user's location. Subsequently, the selected Tx is used to establish a point-to-point channel allocation, hence forming a closed-access zone within a certain effective communication range. Through numerical simulations, it is observed that the single Tx-based VLC transmission confines the legitimate user in a pre-defined trust boundary for a secure transmission.
| Original language | English |
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| Title of host publication | 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) |
| Publisher | IEEE |
| Pages | 506-511 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350348743 |
| ISBN (Print) | 9798350348750 |
| DOIs | |
| Publication status | Published - 19 Jul 2024 |
| Event | 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) - Rome, Italy Duration: 17 Jul 2024 → 19 Jul 2024 Conference number: 14 |
Conference
| Conference | 2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) |
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| Abbreviated title | CSNDSP |
| Country/Territory | Italy |
| City | Rome |
| Period | 17/07/24 → 19/07/24 |
Keywords
- physical layer security (PLS)
- channel allocation
- visible light communications (VLC)
- machine learning (ML)
- probabilistic neural network (PNN)