自组织和可持续的5G蜂窝网络的核心网络管理程序
Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks
摘要：
未来的移动网络（MN），5G和5G之后，将需要从传统资源分配机制的范式转变，因为基站（BS）将被赋予计算能力（即，卸载和计算更靠近移动用户执行）。这是由于网络边缘的普遍移动和物联网（IoT）设备所产生的预期数据量、种类和速度的爆炸性增长。为了实现有效的资源管理，在多访问边缘计算（MEC）范式中，我们利用长短期记忆（LSTM）神经网络进行时间序列预测，并利用控制理论技术进行前瞻性优化，从而为网络边缘内的网络资源处理带来智能机制。在这里，我们提出了在线算法，用于自动缩放和重新配置虚拟化计算平台内的计算加通信资源，并且还通过考虑预测的流量负载和收获的能量来实现动态打开/关闭BS。其主要目标是在保证服务质量（QoS）的前提下，最大限度地降低整体能耗。我们通过跟踪驱动模拟获得的数值结果表明，所提出的优化策略大大减少了边缘计算和通信设施消耗的能源，通过使用绿色能源促进了MN内的能源自我可持续性。
英文摘要：
Future Mobile Networks (MNs), 5G and beyond 5G, will require a paradigm shift from traditional resource allocation mechanisms as Base Stations (BSs) will be empowered with computation capabilities (i.e., offloading and computation is performed closer to mobile users). This is motivated by the expected data explosion in the volume, variety, and velocity, generated by pervasive mobile and Internet of Things (IoT) devices at the network edge. Towards efficient resource management, within the Multi-access Edge Computing (MEC) paradigm, we make use of the Long Short-Term Memory (LSTM) neural network for time series forecasting and control-theoretic techniques for foresighted optimization, thus bringing intelligent mechanisms for handling network resources within the network edge. Here, we propose online algorithms for autoscaling and reconfiguring the computing-plus-communication resources within the virtualized computing platform, and also enable dynamic switching on/off BSs by taking into account the forecasted traffic load and harvested energy. The main goal is to minimize the overall energy consumption, with a guarantee of Quality of Service (QoS). Our numerical results, obtained through trace-driven simulations, show that the proposed optimization strategies lead to a considerable reduction in the energy consumed by the edge computing and communication facilities, promoting energy self-sustainability within the MN through the use of green energy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
