Abstract
This work proposes a Deep Learning approach to help mobile operators plan neighborhood relationships within their network in the form of an application allowing to map sites, sectors and their cells, to calculate the neighbors of different cells based on deep learning algorithms and integrate new sectors or sites on the network. By leveraging complex machine learning techniques, the tool developed can identify patterns, trends and hidden relationships between different base stations in the network. This leads to more effective and efficient planning of neighbor relationships, thereby reducing interference and improving quality of service for subscribers. Additionally, the tool offers increased flexibility and adaptability. It is able to adjust to rapid network changes, such as adding new base stations or changing a network site's settings. This ability to adapt guarantees continuous and optimal planning of neighborhood relations, even in a dynamic and evolving environment.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Engineering and Management Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
