Massive Data Generation for Deep Learning-aided Wireless Systems Using Meta Learning and Generative Adversarial Network
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2022
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910443141857280 |
|---|---|
| author | Kim, Jinhong Ahn, Yongjun Shim, Byonghyo |
| author_facet | Kim, Jinhong Ahn, Yongjun Shim, Byonghyo |
| contents | As an entirely-new paradigm to design the communication systems, deep learning (DL), an approach that the machine learns the desired wireless function, has received much attention recently. In order to fully realize the benefit of DL-aided wireless system, we need to collect a large number of training samples. Unfortunately, collecting massive samples in the real environments is very challenging since it requires significant signal transmission overhead. In this paper, we propose a new type of data acquisition framework for DL-aided wireless systems. In our work, generative adversarial network (GAN) is used to generate samples approximating the real samples. To reduce the amount of training samples required for the wireless data generation, we train GAN with the help of the meta learning. From numerical experiments, we show that the DL model trained by the GAN generated samples performs close to that trained by the real samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_11910 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Massive Data Generation for Deep Learning-aided Wireless Systems Using Meta Learning and Generative Adversarial Network Kim, Jinhong Ahn, Yongjun Shim, Byonghyo Information Theory As an entirely-new paradigm to design the communication systems, deep learning (DL), an approach that the machine learns the desired wireless function, has received much attention recently. In order to fully realize the benefit of DL-aided wireless system, we need to collect a large number of training samples. Unfortunately, collecting massive samples in the real environments is very challenging since it requires significant signal transmission overhead. In this paper, we propose a new type of data acquisition framework for DL-aided wireless systems. In our work, generative adversarial network (GAN) is used to generate samples approximating the real samples. To reduce the amount of training samples required for the wireless data generation, we train GAN with the help of the meta learning. From numerical experiments, we show that the DL model trained by the GAN generated samples performs close to that trained by the real samples. |
| title | Massive Data Generation for Deep Learning-aided Wireless Systems Using Meta Learning and Generative Adversarial Network |
| topic | Information Theory |
| url | https://arxiv.org/abs/2208.11910 |