Deep Learning Based Load Balancing for improved QoS towards 6G

TL;DR

This paper proposes a deep learning-based load balancing algorithm using LSTM neural networks to optimize base station coverage and improve QoS in future 6G wireless networks, demonstrating significant load variance reduction.

Contribution

It introduces a novel LSTM-based load balancing method tailored for 6G heterogeneous networks, enhancing network adaptability and performance.

Findings

LVC decreased by over 98% in tested scenarios

Load balancing improved QoS and load distribution

Effective in heterogeneous wireless network layouts

Abstract

Deep learning has made great strides lately with the availability of powerful computing machines and the advent of user-friendly programming environments. It is anticipated that the deep learning algorithms will entirely provision the majority of operations in 6G. One such environment where deep learning can be the right solution is load balancing in future 6G intelligent wireless networks. Load balancing presents an efficient, cost-effective method to improve the data process capability, throughput, and expand the bandwidth, thus enhancing the adaptability and availability of networks. Hence a load balancing algorithm based on Long Short Term Memory(LSTM) deep neural network is proposed through which the coverage area of base station changes according to geographic traffic distribution, catering the requirement for future generation 6G heterogeneous network. The LSTM model performance is evaluated by considering three different scenarios, and the results were presented. Load variance coefficient(LVC) and load factor(LF) are introduced and validated over two wireless network layouts(WNL) to study the Quality of Service(QoS) and load distribution. The proposed method shows a decrease of LVC by 98.311% and 99.21% for WNL1, WNL2 respectively.