Abstract
In recent years, many power allocation algorithms to maximize energy efficiency (EE) have emerged in wireless communication systems (WCS), but these traditional power allocation algorithms have high computational complexity. The advanced deep learning technique proposed in this paper is shown to solve the transmission power control problem in wireless networks to optimize EE. From a machine learning perspective, the conventional power allocation algorithms can be viewed as a nonlinear mapping between channel gains among users and the optimal power allocation scheme, and deep neural network (DNN) can be trained to learn this nonlinear mapping. Based on this, a DNN-based power allocation method is proposed, and the specific structure of the DNN and the system model of the DNN method are introduced to maximize the EE among users in WCS. The results show that the performance of the proposed method using DNN is essentially the same as that achieved by the conventional algorithm, but the computational time is greatly reduced.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (61901301, 61701345), and the Natural Science Foundation of Tianjin (18JCZDJC31900).
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Dong, K., Han, L., Li, Y. (2023). Maximizing Energy-Efficiency in Wireless Communication Systems Based on Deep Learning. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. AIC 2022. Lecture Notes in Electrical Engineering, vol 871. Springer, Singapore. https://doi.org/10.1007/978-981-99-1256-8_41
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DOI: https://doi.org/10.1007/978-981-99-1256-8_41
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