1. ASRE-KG&RS: knowledge graph and recommender system for adaptive smart radio environment.
- Author
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Luo, Wenyu, Yan, Tianze, Xuan, Annan, Hou, Changxing, and Shao, Xia
- Abstract
With the rapid advancement of wireless communication technologies, efficient utilization of the spectrum has become more complex and competitive. Millimeter-waveand Terahertz are considered to be one of the key technologies for next-generation wireless communication systems. However, high-frequency LOS links are sensitive to occlusions, which may lead to blocking problems during signal transmission. Therefore, in this study, we propose a recommendation system based on the knowledge graph of adaptive smart radio environment, which aims to solve the blocking problem that is prone to occur in the new generation of communication. The system utilizes deep learning and knowledge graph techniques, as well as sensing and understanding of the radio environment, to provide users with personalized communication services. First, it obtains information about the user's location, network state, and surroundings by sensing and understanding the wireless environment, second, it constructs a knowledge graph using the sensed information, and then, it extracts effective feature representations from the knowledge graph, including the user's communication history, device type, and network congestion, using deep learning techniques. Based on these features, the system generates personalized recommendations, such as optimizing the allocation of communication resources or selecting the best communication policy. By continuously learning and optimizing the wireless environment, the recommender system can provide more efficient and reliable communication services to solve the blocking problem in the new generation of wireless communications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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