Abstract
The emergence of artificial intelligence (AI)-based methods evolving from 5G to 6G is accelerating. Therefore, to optimize the communication system in the 6G era, it is essential to adapt several AI-based optimization methods according to each environment. In this chapter, we introduce two general AI-based optimization methods, named AI-aided and AI-native. In addition, we describe the pros and cons of each method. Finally, by illustrating previous studies on AI in communication, we aim to speed up the development of both AI-based and AI-native optimization methods in line with the upcoming 6G era.
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References
Lim, Byungju, Yun, Won Joon, Kim, Joongheon Kim, and Ko, Young-Chai (2022) Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments In IEEE Transactions on Vehicular Technology
S. Dörner, S. Cammerer, J. Hoydis and S. T. Brink, “Deep learning based communication over the air,” IEEE Journal of Selected Topics in Signal Processing, pp.132–143, 2017
T.J. O’Shea and J. Hoydis, “An Introduction to deep learning for the physical layer,” IEEE Transactions on Cognitive Communications and Networking, pp. 563–575, 2017
West, Nathan E. and T. O’shea, “Deep architectures for modulation recognition,” IEEE International Symposium on Dynamic Spectrum Access Networks, pp. 1–6, 2017
L. Ma, Y. Yang and H. Wang, “DBN based automatic modulation recognition for ultra-low SNR RFID signals,” in Proc. of the IEEE Chinese Control Conference (CCC), pp.7054–7057, 2016
Y. Zeng, M. Zhang, F. Han, Y. Gong and J. Zhang, “Spectrum analysis and convolutional neural network for automatic modulation recognition,” IEEE Wireless Communications Letters, pp.929–932, 2019
F. Wang, Y. Wang and X. Chen, “Graphic constellations and DBN based automatic modulation classification,” Proc. of the IEEE 85th Vehicular Technology Conference (VTC Spring), pp. 1–5, 2017
T. Gruber, S. Cammerer, J. Hoydis and S. TenBrink, “On deep learning-based channel decoding”, Proc. of the IEEE Annual Conference on Information Sciences and Systems (CISS), pp.1–6, 2017
F. Liang, C. Shen, F. Wu, “An iterative BP-CNN architecture for channel decoding”, IEEE Journal of Selected Topics in Signal Processing, pp. 144–159, 2018
, T. Wang, L. Zhang, S. C. Liew “Deep learning for joint MIMO detection and channel decoding”, Proc. of the IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1–7, 2019
M. Honkala, D. Korpi and J. M. Huttunen, “DeepRx: Fully convolutional deep learning receiver,” IEEE Transactions on Wireless Communications, pp. 3925–3940, 2021
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1369, 2010
J.Choi, M. Yerzhanova, J. Park, Y. H. Kim, “Deep Learning Driven Beam Selection for Orthogonal Beamforming with Limited Feedback,” ICT Express, vol. 8, no. 3, pp. 473–478, 2022.
C. Zou, F. Yang, J. Song and Z. Han, “Channel Autoencoder for Wireless Communication: State of the Art, Challenges, and Trends,” IEEE Communications Magazine, vol. 59, no. 5, pp. 136–142, 2021.
T. J. O’Shea, K. Karra, and T. C. Clancy, “Learning to Communicate: Channel Auto-Encoders, Domain Specific Regularizers, and Attention,” Proc. of IEEE Int’l. Symp. Signal Processing and Info. Technology, Limassol, Cyprus, pp. 223–28, December 2016.
T. J. O’Shea, T. Erpek, and T. C. Clancy, “Deep Learning Based MIMO Communications,” CoRR vol. abs/1707.07980, 2017; http://arxiv.org/abs/1707.07980.
A. Felix et al., “OFDM-Autoencoder for End-to-End Learning of Communications Systems,” Proc. of IEEE 19th Int’l. Wksp. Signal Processing Advances in Wireless Commun., Kalamata, Greece, June 2018.
S. Dorner et al., “Deep Learning Based Communication Over the Air,” IEEE J. Sel. Top. Sign. Process., vol. 12, no. 1, pp. 132–143, 2018.
H. Zhang et al., “Non-Coherent Energy-Modulated Masive SIMO in Multipath Channels: A Machine Learning Approach,” IEEE Internet of Things J., vol. 7, no. 9, pp. 8263–8270, 2020.
M. Stark, F. Ait Aoudia and J. Hoydis, “Joint Learning of Geometric and Probabilistic Constellation Shaping,” Proc. of 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA, December 2019.
V. Raj and S. Kalyani, “Design of Communication Systems Using Deep Learning: A Variational Inference Perspective,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 4, pp. 1320–1334, 2020.
H. Ye, L. Liang, G. Y. Li and B. -H. Juang, “Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels,” IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3133–3143, 2020.
V. Raj and S. Kalyani, “Backpropagating Through the Air: Deep Learning at Physical Layer Without Channel Models,” IEEE Communications Letters, vol. 22, no. 11, pp. 2278–2281, 2018.
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Baek, H., Lee, H., Park, S., Lee, H., Park, J., Kim, J. (2024). AI-Native Communications. In: Lin, X., Zhang, J., Liu, Y., Kim, J. (eds) Fundamentals of 6G Communications and Networking. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-37920-8_21
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DOI: https://doi.org/10.1007/978-3-031-37920-8_21
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