Browse > Article
http://dx.doi.org/10.3837/tiis.2022.10.014

Modulation Recognition of MIMO Systems Based on Dimensional Interactive Lightweight Network  

Aer, Sileng (College of Information and Communication Engineering, Harbin Engineering University)
Zhang, Xiaolin (College of Information and Communication Engineering, Harbin Engineering University)
Wang, Zhenduo (College of Information and Communication Engineering, Harbin Engineering University)
Wang, Kailin (College of Information and Communication Engineering, Harbin Engineering University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.10, 2022 , pp. 3458-3478 More about this Journal
Abstract
Automatic modulation recognition is the core algorithm in the field of modulation classification in communication systems. Our investigations show that deep learning (DL) based modulation recognition techniques have achieved effective progress for multiple-input multiple-output (MIMO) systems. However, network complexity is always an additional burden for high-accuracy classifications, which makes it impractical. Therefore, in this paper, we propose a low-complexity dimensional interactive lightweight network (DilNet) for MIMO systems. Specifically, the signals received by different antennas are cooperatively input into the network, and the network calculation amount is reduced through the depth-wise separable convolution. A two-dimensional interactive attention (TDIA) module is designed to extract interactive information of different dimensions, and improve the effectiveness of the cooperation features. In addition, the TDIA module ensures low complexity through compressing the convolution dimension, and the computational burden after inserting TDIA is also acceptable. Finally, the network is trained with a penalized statistical entropy loss function. Simulation results show that compared to existing modulation recognition methods, the proposed DilNet dramatically reduces the model complexity. The dimensional interactive lightweight network trained by penalized statistical entropy also performs better for recognition accuracy in MIMO systems.
Keywords
automatic modulation recognition; multiple-input multiple-output(MIMO); lightweight network; two-dimensional interactive attention; penalized statistical entropy;
Citations & Related Records
연도 인용수 순위
  • Reference
1 K. Bu, Y. He, X. Jing, and J. Han, "Adversarial Transfer Learning for Deep Learning Based Automatic Modulation Classification," IEEE Signal Process Lett., vol. 27, pp. 880-884, 2020.   DOI
2 C. -F. Teng, C. -Y. Chou, C. -H. Chen, and A. -Y. Wu, "Accumulated Polar Feature-Based Deep Learning for Efficient and Lightweight Automatic Modulation Classification with Channel Compensation Mechanism," IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 15472-15485, Dec. 2020.   DOI
3 L. Li, J. Huang, Q. Cheng, H. Meng, and Z. Han, "Automatic Modulation Recognition: A FewShot Learning Method Based on the Capsule Network," IEEE Wireless Commun Lette., vol. 10, no. 3, pp. 474-477, March 2021.   DOI
4 C. Lin, W. Yan, L. Zhang, and Y. Wang, "An Overview of Communication Signals Modulation Recognition," J. China. Academy. Electron. Inform. Technol., vol. 16, no. 11, pp. 1074-1085, Nov 2021.
5 X. Zhang, J. Sun, and X. Zhang, "Automatic Modulation Classification Based on Novel Feature Extraction Algorithms," IEEE Access, vol. 8, pp. 16362-16371, 2020.   DOI
6 J. Fu, J. Liu, and H. Tian, "Dual Attention Network for Scene Segmentation," in Proc. of IEEE/CVF. Conf. Computer Vision. Pattern Recognition, pp. 3141-3149, June 2019.
7 J. He, and W. Zhang, "Communication Signal Modulation Recognition Technology and Its Development," High. Technol. lett., vol. 26, no. 2, pp. 157-165, Feb 2016.
8 Y. Wang, J. Yang, M. Liu, and G. Gui, "LightAMC: Lightweight Automatic Modulation Classification Via Deep Learning and Compressive Sensing," IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 3491-3495, March 2020.   DOI
9 M. Turan, M. Oner, and H. A. Cirpan, "Joint Modulation Classification and Antenna Number Detection for MIMO Systems," IEEE Commun. lett., vol. 20, no. 1, pp. 193-196, Jan. 2016.   DOI
10 X. Yan, G. Liu, H. Wu, G. Zhang, Q. Wang, and Y. Wu, "Robust Modulation Classification Over α-Stable Noise Using Graph-Based Fractional Lower-Order Cyclic Spectrum Analysis," IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 2836-2849, January. 2020.   DOI
11 S. Hu, Y. Pei, P. P. Liang, and Y. Liang, "Deep Neural Network for Robust Modulation Classification under Uncertain Noise Conditions," IEEE Trans. Veh. Technol., vol. 69, no. 1, pp. 564-577, Jan. 2020.   DOI
12 Z. Liang, M. Tao, L. Wang, J. Su, and X. Yang, "Automatic Modulation Recognition Based on Adaptive Attention Mechanism and ResNeXt WSL Model," IEEE Commun. lett., vol. 25 pp. 2953-2957, Sept 2021.   DOI
13 Y. Wang, J. Gui, Y. Yin, J. Wang, and G. Gui, "Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization," IEEE Trans. Veh. Technol., vol. 69, no. 5, pp. 5688-5692, May 2020.   DOI
14 W. Shi, D. Liu, X. Cheng, Y. Li, and Y. Zhao, "Particle Swarm Optimization-Based Deep Neural Network for Digital Modulation Recognition," IEEE Access, vol. 7, pp. 104591-104600, 2019.   DOI
15 X. Zhao, C. Guo, and J. Li, "Mixed Recognition Algorithm for Signal Modulation Schemes by High-order Cumulants and Cyclic Spectrum," (in Chinese), J. Electron. Inform. Technol., vol. 38, no. 3, pp. 674-680, Mar 2016.
16 C. Wu, and G. Feng, "New Automatic Modulation Classifier Using Cyclic-spectrum Graphs with Optimal Training Features," IEEE Commun. lett., vol. 22, no. 6, pp. 1204-1207, June 2018.   DOI
17 S. Kalluri and G. R. Arce, "Adaptive Weighted Myriad Filter Algorithms for Robust Signal Processing in /Spl Alpha/-Stable Noise Environments," IEEE Trans Signal Process., vol. 46, no. 2, pp. 322-334, Feb. 1998.   DOI
18 C. Zhang, S. Yu, G. Li, and Y. Xu, "The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm," IEEE Access, vol. 9, pp. 36078-36086, 2021.   DOI
19 Y. Mao, Y. -Y. Dong, T. Sun, X. Rao, and C. -X. Dong, "Attentive Siamese Networks for Automatic Modulation Classification Based on Multitiming Constellation Diagrams," IEEE Trans. Neural Netw. Learn Systems., pp. 1-15, 2021.
20 J. B. Tamakuwala, "New Low Complexity Variance Method for Automatic Modulation Classification and Comparison with Maximum Likelihood Method," in Proc. of 2019 International Conference on Range Technology (ICORT), Balasore, India, pp. 1-5, February. 2019.
21 T. O'Shea, and J. Hoydis, "An Introduction to Deep Learning for The Physical Layer," IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 4, pp. 563-575, Dec. 2017.   DOI
22 T. V. R. O. Camara, A. D. L. Lima, B. M. M. Lima, A. I. R. Fontes, A. D. M. Martins, and L. F. Q. Silveira, "Automatic Modulation Classification Architectures Based on Cyclostationary Features in Impulsive Environments," IEEE Access, vol. 7, pp. 138512-138527, Sep. 2019.   DOI
23 H. Gao, S. Wang, Y. Su, H. Sun, and Z. Zhang, "Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise," KSII Trans. Internet. Inform. Systems., vol. 15, pp. 2356-2376, July. 2021.
24 D. Das, P. K. Bora, and R. Bhattacharjee, "Blind Modulation Recognition of the Lower Order PSK Signals under the MIMO Keyhole Channel," IEEE Commun. lett., vol. 22, no. 9, pp. 1834-1837, Sept. 2018.   DOI
25 T. J. O'Shea, T. Roy, and T. C. Clancy, "Over-the-air Deep Learning Based Radio Signal Classification," IEEE J. Sel. Topics Signal Process., vol. 12, no. 1, pp. 168-179, Feb. 2018.   DOI
26 Y. Wang, G. Gui, T. Ohtsuki, and F. Adachi, "Multi-task Learning for Generalized Automatic Modulation Classification under Non-gaussian Noise with Varying SNR Conditions," IEEE Trans. Wireless Commun., vol. 20, no. 6, pp. 3587-3596, June 2021.   DOI
27 Y. Wang, J. Wang, W. Zhang, J. Yang, and G. Gui, "Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems," IEEE Trans. Veh. Technol., vol. 69, no. 4, pp. 4575-4579, April 2020.   DOI
28 Dhamyaa H. Al-Nuaimi, Ivan A. Hashim, Intan S. Zainal Abidin, Laith B. Salman, and Nor Ashidi Mat Isa, "Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification-A Review," electronics., vol. 8, no. 12, pp. 1407, Nov 2019.
29 M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. -C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in Proc. of IEEE/CVF. Conf. Computer Vision. Pattern Recognition, pp. 4510-4520, June 2018.