基于深度学习的负载均衡，用于改进的面向6G的QoS
Deep Learning Based Load Balancing for improved QoS towards 6G
摘要：
随着强大的计算机器的可用性和用户友好的编程环境的出现，深度学习最近取得了长足的进步。预计深度学习算法将完全提供6G中的大部分操作。深度学习可以成为正确解决方案的一个环境是未来6G智能无线网络中的负载均衡。负载均衡是一种高效、经济的方法，可以提高网络的数据处理能力和吞吐量，扩展网络带宽，从而增强网络的适应性和可用性。为此，提出了一种基于长短期记忆（LSTM）深度神经网络的负载均衡算法，通过该算法，基站的覆盖区域根据地理流量分布而变化，以满足下一代6G异构网络的需求。通过考虑三种不同的场景来评估LSTM模型的性能，并给出了结果。为了研究无线网络的服务质量和负载分布，引入了负载方差系数和负载因子，并在两种无线网络布局下进行了验证。该方法对WNL 1和WNL 2的LVC分别降低了98.311%和99.21%。
英文摘要：
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.
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