Automatic Modulation Recognition Using Generative Adversarial Networks

In the realm of spectrum sensing, the demand for automatic modulation recognition (AMR) has intensified due to the scarcity of spectrum resources. This study delves into the utilization of Generative Adversarial Networks (GAN) to automate AMR, a departure from manual methods. By employing GAN's generator and discriminator components, the modulation classification process is enhanced, allowing for the exploration of unfamiliar modulation tasks and signals. The integration of GAN with AMR presents a promising avenue for spectrum resource allocation and modulation recognition in wireless communication systems. The literature review encompasses pivotal works in the fields of AMR and GAN, highlighting significant contributions and advancements made by researchers over the years. The dataset utilized consists of synthetic data comprising various modulations at differing signal-to-noise ratios, providing a robust foundation for training and testing AMR models. The feature extraction and model construction processes are intricately detailed, emphasizing the key components involved in achieving accurate automatic modulation recognition using generative adversarial networks.

• Spectrum Sensing
• Modulation Recognition
• Generative Adversarial Networks
• Wireless Communication
• Deep Learning

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1. Automatic Modulation Recognition Using Generative Adversarial Networks Group 4 Jiawei Yin, Jinglong Du, Ziwen Li

2. 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)

3. 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).

4. 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.

5. 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]

6. 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

7. 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

8. Model Construction Noise Input Feature Extraction Classifier(C) Fake samples z Data Input Encoder(E) Generator(G) Real samples Discrimator(D)

9. 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

10. 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.

11. Current Results The final classifier evaluation loss of 110000 evaluation test data is 7.728.

12. 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.

13. 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.