Graph Neural Network-based End-to-End Learning for Multi-User MIMO Systems
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911455725486080 |
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| author | Chang, Hao Vo, Hoang Triet Kosasih, Alva Vucetic, Branka Hardjawana, Wibowo |
| author_facet | Chang, Hao Vo, Hoang Triet Kosasih, Alva Vucetic, Branka Hardjawana, Wibowo |
| contents | End-to-end (E2E) learning has recently been proposed to jointly design the modulator and symbol detector by using deep neural networks (DNNs). However, existing schemes lack sufficient capability to cancel multi-user interference (MUI) in uplink multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we propose a graph neural network (GNN)-based E2E learning scheme that employs a GNN-based modulator to generate learned constellation points, and a GNN-based detector to cancel MUI. They are jointly optimized to minimize the symbol error rate (SER) performance loss. Simulation results demonstrate that the proposed E2E outperforms existing schemes with a predefined modulator. Specifically, it achieves an approximate 2 dB gain in a high MUI environment and surpasses even the maximum-likelihood (ML) detector in a low MUI condition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16112 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network-based End-to-End Learning for Multi-User MIMO Systems Chang, Hao Vo, Hoang Triet Kosasih, Alva Vucetic, Branka Hardjawana, Wibowo Signal Processing End-to-end (E2E) learning has recently been proposed to jointly design the modulator and symbol detector by using deep neural networks (DNNs). However, existing schemes lack sufficient capability to cancel multi-user interference (MUI) in uplink multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we propose a graph neural network (GNN)-based E2E learning scheme that employs a GNN-based modulator to generate learned constellation points, and a GNN-based detector to cancel MUI. They are jointly optimized to minimize the symbol error rate (SER) performance loss. Simulation results demonstrate that the proposed E2E outperforms existing schemes with a predefined modulator. Specifically, it achieves an approximate 2 dB gain in a high MUI environment and surpasses even the maximum-likelihood (ML) detector in a low MUI condition. |
| title | Graph Neural Network-based End-to-End Learning for Multi-User MIMO Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2411.16112 |