Node Position Estimation in Diffusion-Based Molecular Communications Using Multi-Layer Perceptron
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916942322860032 |
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| author | Hwang, Sangjun Chae, Chan-Byoung |
| author_facet | Hwang, Sangjun Chae, Chan-Byoung |
| contents | This paper proposes a method for accurately estimating the relative position between two nodes with unknown locations in a diffusion-based molecular communication environment. A specialized node structure is designed, combining a central absorbing receiver with multiple transmitters placed at predefined spherical coordinates. Pilot molecules are released, and their absorption time and concentration are measured. By partitioning the spherical coordinate space, these spatially distinct measurements serve as input to a multilayer perceptron (MLP)-based model. The proposed method significantly improves the precision of distance and direction estimation. Simulation results demonstrate localization accuracy, confirming the effectiveness of the neural network model in capturing the underlying physical characteristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Node Position Estimation in Diffusion-Based Molecular Communications Using Multi-Layer Perceptron Hwang, Sangjun Chae, Chan-Byoung Signal Processing 94A12 C.2.1 This paper proposes a method for accurately estimating the relative position between two nodes with unknown locations in a diffusion-based molecular communication environment. A specialized node structure is designed, combining a central absorbing receiver with multiple transmitters placed at predefined spherical coordinates. Pilot molecules are released, and their absorption time and concentration are measured. By partitioning the spherical coordinate space, these spatially distinct measurements serve as input to a multilayer perceptron (MLP)-based model. The proposed method significantly improves the precision of distance and direction estimation. Simulation results demonstrate localization accuracy, confirming the effectiveness of the neural network model in capturing the underlying physical characteristics. |
| title | Node Position Estimation in Diffusion-Based Molecular Communications Using Multi-Layer Perceptron |
| topic | Signal Processing 94A12 C.2.1 |
| url | https://arxiv.org/abs/2509.07441 |