Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques
نویسندگان
چکیده
Current LTE networks are experiencing significant growth in the number of users worldwide. The use data services for online browsing, e-learning, meetings and initiatives such as smart cities means that subscribers stay connected long periods, thereby saturating a signalling resources. One resources is Radio Resource Connected (RRC) parameter, which allocated to eNodeBs with aim limiting simultaneously network. fixed allocation this parameter that, depending on traffic at different times day geographical position, some saturated RRC (overused) while others have unused However, these limited, there problem their underutilization (non-optimal utilization eNodeB level) due static (manual configuration resources). objective paper design an efficient machine learning model will take input key performance indices (KPIs) like data, RRC, simultaneous users, etc., each per hour accurately predict needed be dynamically them order avoid financial losses mobile network operator. To reach target, three algorithms been studied namely: linear regression, convolutional neural short-term memory (LSTM) train models evaluate them. trained LSTM algorithm gave best 97% accuracy was therefore implemented proposed solution resource allocation. An interconnection architecture also embed into Operation maintenance In way, can contribute developing expanding concept Self Organizing Network (SON) used 4G 5G networks.
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ژورنال
عنوان ژورنال: Journal of computer and communications
سال: 2023
ISSN: ['2327-5219', '2327-5227']
DOI: https://doi.org/10.4236/jcc.2023.116005