The next-generation wireless networks are required to satisfy a variety of services and criteria concurrently. To address upcoming strict criteria, a new open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is being investigated as a critical strategy for ensuring network quality of service (QoS) in the face of changing circumstances. However, distinct network slices must be dynamically controlled to avoid service level agreement (SLA) variation caused by rapid changes in the environment. Therefore, this paper introduces a novel framework able to manage the network slices through provisioned resources intelligently. Due to diverse heterogeneous environments, intelligent machine learning approaches require sufficient exploration to handle the harshest situations in a wireless network and accelerate convergence. To solve this problem, a new solution is proposed based on evolutionary-based deep reinforcement learning (EDRL) to accelerate and optimize the slice management learning process in the radio access network's (RAN) intelligent controller (RIC) modules. To this end, the O-RAN slicing is represented as a Markov decision process (MDP) which is then solved optimally for resource allocation to meet service demand using the EDRL approach. In terms of reaching service demands, simulation results show that the proposed approach outperforms the DRL baseline by 62.2%.
翻译:下一代无线网络需要同时满足各种服务和标准。为了应对即将到来的严格标准,已经开发出一个新的开放的无线电接入网络(O-RAN),具有灵活设计、分类的虚拟和可编程组件、智能闭路控制等不同特点。正在对O-RAN切片进行调查,作为在不断变化的环境下确保网络服务质量的关键战略,但不同的网络切片必须进行动态控制,以避免由于环境的迅速变化而导致的服务级协议变化。因此,本文件引入了一个能够通过提供智能资源来管理网络的新型框架。由于不同的环境,智能机器学习方法需要充分探索,以便在无线网络中处理最严酷的情况并加速趋同。为解决这一问题,在基于进化的深层强化学习(EDRL)的基础上提出了一个新的解决方案,以加速和优化无线电接入网络智能控制器模块中的切片管理学习进程。为此,O-RAN 剪切是一个能够通过智能资源共享的配置来进行切片切分切的新的框架。由于各种不同的环境,智能环境,智能机器学习方法需要充分探索,在无线网络中处理最困难的情况,因此,需要用IMOL格式的配置资源配置方法,从而满足对ERDRRRS的配置进行最佳的配置。