Automatic Modulation Recognition Using GANs: A Literature Review
Explore the application of Generative Adversarial Networks (GANs) for automatic modulation recognition (AMR) in wireless communication. Understand the significance of AMR in spectrum sensing and the use of GANs for modulation classification. Delve into relevant literature, dataset details, and feature extraction methods in this innovative field of study.
Uploaded on | 0 Views
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
Automatic Modulation Recognition Using Generative Adversarial Networks Group 4 Jiawei Yin, Jinglong Du, Ziwen Li
Background In recent years, due to the increasing number of fixed spectrum allocation and wireless devices, spectrum resources become more and more scarce. Spectrum sensing is a technique that help to allocate the limited resources A key enabler in spectrum sensing is automatic modulation recognition(AMR)
Background In the past, AMR is done by the manual work. To expand AMR technique into unfamiliar tasks and signals, Generative Adversarial Networks (GAN) is introduced to help do the modulation classification. GAN plays a min-max game which includes a generator(G) and a discriminator(D).
Literature Review AMR 1996, Azzouz et al. Automatic Modulation Recognition of Communication Signals. [1] 2016, O Shea et al. propose a method to apply CNN to the modulation recognition field[2] and use time-domain in-phase orthogonal (IQ) signal as the input of the network. 2018, Li et al. Robust Automated VHF Modulation Recognition Based on Deep Convolutional Neural Networks.[3] 2018, Bin et al. [4] successfully linked signal processing with computer vision by application of GAN.
Literature Review GAN 2014, Goodfellow et al. Generative Adversarial Network(GAN)[5] 2014, Mirza et al. conditional generative adversarial network (CGAN)[6] 2016, Radford et al. Deep Convolutional Generative Adversarial Network (DCGAN)[7]
Dataset A synthetic dataset consists of 11 modulations (8 digital and 3 analog) at varying signal-to-noise ratios, ranging from 20 dB to 20 dB.* Size: 110000*2*128(train input), 110000*1(train label) 110000*2*128(test input), 110000*1(test label) Each modulation has 20000 examples * The dataset is available from: https://www.deepsig.ai/datasets
Feature Extraction Input Encoder(E) Z exp Encoder(E) Normal Distribution Layer Type Input Shape s Reshape 2*128 LSTM 128*2 Dense 10 Dense 100
Model Construction Noise Input Feature Extraction Classifier(C) Fake samples z Data Input Encoder(E) Generator(G) Real samples Discrimator(D)
Model Construction Encoder(E) Generator(G) Classifier(C) Discriminator(D) Layer Type Input Shape Layer Type Input Shape Layer Type Input Shape Layer Type Input Shape Reshape 2*128 Dense 100 Reshape 2*128 Reshape 2*128 LSTM 128*2 Reshape 8192 Conv2D 2*128*1 Conv2D 2*128*1 Dense 10 Conv2DTranspose 2*128*32 AveragePooling2D 2*128*32 AveragePooling2D 2*128*32 Dense 100 Conv2DTranspose 2*128*256 Conv2D 2*64*32 Conv2D 2*64*32 Custom Layer 100 Conv2DTranspose 2*128*80 AveragePooling2D 2*64*32 AveragePooling2D 2*64*32 Reshape 2*128*1 Flatten 2*32*32 Flatten 2*32*32 Dense 2048 Dense 2048
Current Results The loss of generator and discriminator is shown below. The learning rate is 0.004 for C and 0.001 for G and D. We use Adam as the optimizer for all the model. The loss function for G i is mean squared error, for C is sparse categorical crossentropy and for D is binary crossentropy.
Current Results The final classifier evaluation loss of 110000 evaluation test data is 7.728.
Further Work Preprocess the data to get better result. Solve the coding problem to get the accuracy of our trained model. Compare our approach with other DL methods.
Reference [1]: Azzouz, E.E.; Nandi, A.K. Automatic Modulation Recognition of Communication Signals. IEEE Trans. Commun. 1996, 46, 431 436. [2]: O Shea, T.J.; Hoydis, J. An Introduction to Deep Learning for the Physical Layer. IEEE Trans. Cognit. Commum. Netw. 2017, 3, 563 575. [3]: Li, R.; Li, L.; Yang, S.; Li, S. Robust Automated VHF Modulation Recognition Based on Deep Convolutional Neural Networks. IEEE Commun. Lett. 2018, 22, 946 949. [4]: Tang, B.; Tu, Y.; Zhang, Z.; Lin, Y. Digital Signal Modulation Classification With Data Augmentation Using Generative Adversarial Nets in Cognitive Radio Networks. IEEE Access 2018, 6, 15713 15722. [5]: Goodfellow, I.J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative Adversarial Networks. Adv. Neural Inf. Process. Syst. 2014, 3, 2672 2680. [6]: Mirza, M.; Osindero, S. Conditional Generative Adversarial Nets. In Proceedings of the Neural Information Processing Systems (NIPS), Montreal, QC, Canada, 8 13 December 2014. [7]: Radford, A.; Metz, L.; Chintala, S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In Proceedings of the International Conference on Learning Representations (ICLR), San Juan, PR, USA, 2 4 May 2016.
