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Deep Learning Based Load Balancing for improved QoS towards 6G

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Document pages: 19 pages

Abstract: Deep learning has made great strides lately with the availability of powerfulcomputing machines and the advent of user-friendly programming environments. Itis anticipated that the deep learning algorithms will entirely provision themajority of operations in 6G. One such environment where deep learning can bethe right solution is load balancing in future 6G intelligent wirelessnetworks. Load balancing presents an efficient, cost-effective method toimprove the data process capability, throughput, and expand the bandwidth, thusenhancing the adaptability and availability of networks. Hence a load balancingalgorithm based on Long Short Term Memory(LSTM) deep neural network is proposedthrough which the coverage area of base station changes according to geographictraffic distribution, catering the requirement for future generation 6Gheterogeneous network. The LSTM model performance is evaluated by consideringthree different scenarios, and the results were presented. Load variancecoefficient(LVC) and load factor(LF) are introduced and validated over twowireless network layouts(WNL) to study the Quality of Service(QoS) and loaddistribution. The proposed method shows a decrease of LVC by 98.311 and 99.21 for WNL1, WNL2 respectively.

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