Abstract
In light of the trends towards modernization, globalization and achieving sustainability, especially in the field of wireless communications technology, the increasing demand for data transfer services, and the development of artificial intelligence technologies, which have become the cornerstone of most applications that seek to achieve sustainability and benefit from artificial intelligence technology, this study aimed to identify and evaluate the impact of using intelligence technologies. Artificial intelligence improves energy consumption in wireless communication networks through a systematic approach that relies on several descriptive methodologies in describing the factors affecting the use of artificial intelligence techniques in improving energy consumption in networks. Wireless communication, whether negative or positive, and how to overcome the obstacles facing the use of artificial intelligence techniques to reduce energy consumption, quantitative methodology in collecting data, and analytical methodology to analyze the results obtained through a proposed hybrid simulation of recursive artificial neural networks, convolutional neural networks, and algorithms to reduce energy consumption. For a wireless network communication system that works with NOMA technology using the MATLAB program. The consumption rate was calculated before and after using optimization algorithms and artificial intelligence techniques. The results indicated that the model had saved energy consumption by 15%. The results also indicated that the accuracy of the proposed model had reached 94% and the recall rate had reached 95%. F-1scor had reached 96%. .
Keywords: ((improving, wireless networks, energy consumption , NOMA , artificial neural networks, CNN,RNN sustainability, accuracy, prediction, f1-scor)
