RecuGAN: A Novel Generative AI Approach for Synthesizing RF Coverage Maps
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2024 33rd International Conference on Computer Communications and Networks (ICCCN), 2024, 00, pp. 1-9
- Issue Date:
- 2024-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| RecuGAN_A_Novel_Generative_AI_Approach_for_Synthesizing_RF_Coverage_Maps.pdf | Published version | 3.07 MB |
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Radio frequency coverage maps RF maps are essential in wireless communication but obtaining them through site surveys can be labor intensive and sometimes impractical To address this challenge we propose RecuGAN a generative adversarial network GAN based approach for generating RF maps RecuGAN leverages the principles of information maximizing GAN InfoGAN to capture latent properties of RF maps enabling unsupervised categorization and generation of new and diverse RF maps Unlike traditional methods RecuGAN does not require labeled data or conditional input reducing complexity time and cost We enhance the RecuGAN objective function with a customized gradient penalty based Wasserstein GAN WGAN function and a gradient based loss function for stable training and accurate map generation We also provide the option to incorporate multiple generators in RecuGAN enabling high resolution RF map generation As demonstrated through extensive training with both experimental and simulation data RecuGAN can synthesize diverse high quality RF maps and categorize them based on the RSS distribution Compared to a UNet based conditional GAN cGAN RecuGAN achieves a mean average percentage error MAPE of 1 18 outperforming the cGAN model which achieves a MAPE of 2 5
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