Deep Learning Algorithm for Maximizing the Spectral Efficiency of Wireless Systems
Author:
Publisher
Pleiades Publishing Ltd
Subject
Computer Vision and Pattern Recognition
Link
https://link.springer.com/content/pdf/10.1134/S1054661822040034.pdf
Reference30 articles.
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2. A. Balatsoukas-Stimming, O. Castañeda, S. Jacobsson, G. Durisi, and Ch. Studer, “Neural-network optimized 1-bit precoding for massive MU-MIMO,” in IEEE 20th Int. Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019 (IEEE, 2019), pp. 1–5. https://doi.org/10.1109/SPAWC.2019.8815519
3. E. Bobrov, A. Markov, and D. Vetrov, “Variational autoencoders for precoding matrices with high spectral efficiency,” (2021). arXiv:2111.15626 [eess.SP]
4. A. Caciularu and D. Burshtein, “Blind channel equalization using variational autoencoders,” in IEEE Int. Conf. on Communications Workshops (ICC Workshops), Kansas City, Mo., 2018 (IEEE, 2018), pp. 1–6. https://doi.org/10.1109/ICCW.2018.8403666
5. P. Dong, H. Zhang, and G. Ye Li, “Framework on deep learning-based joint hybrid processing for mmWave massive MIMO systems,” IEEE Access 8, 106023– 106035 (2020). https://doi.org/10.1109/ACCESS.2020.3000601
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