Network slicing technology promises to be a critical enabler of fifth-generation (5G) and sixth-generation (6G) wireless networks, allowing the infrastructure to be divided into several virtual slices. Mobile network operators are focused on developing novel solutions to implement various slices to address diverse use cases and guarantee key performance indicators (KPIs) such as latency, resource utilization, and energy efficiency. Energy efficiency (EE) KPIs are particularly important during slice deployments to ensure a reduced carbon footprint. However, deploying slices with high energy efficiency presents significant challenges. This paper proposes an energy-aware design policy for deploying slices by optimizing energy consumption and deployment capacity. A deep reinforcement learning (DRL) approach is employed, utilizing an actor-critic architecture to train a learning network modeled with a pointer network structure and an attention mechanism. A search strategy is also proposed to refine learning parameters and determine the final design policy. Compared to two existing approaches, the proposed algorithms demonstrate improved performance in terms of energy efficiency and cumulative acceptance ratio. Specifically, the EE KPI achieved by the proposed approach is enhanced to 69.7%.

中文翻译：

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使用深度强化学习进行网络切片的能量感知设计策略


网络切片技术有望成为第五代 （5G） 和第六代 （6G） 无线网络的关键推动因素，允许将基础设施划分为多个虚拟切片。移动网络运营商专注于开发新颖的解决方案来实施各种切片，以解决不同的用例并保证关键性能指标 （KPI），例如延迟、资源利用率和能源效率。能源效率 （EE） KPI 在切片部署期间尤为重要，以确保减少碳足迹。然而，部署具有高能效的切片会带来重大挑战。本文提出了一种通过优化能耗和部署容量来部署 slices 的能源感知设计策略。采用深度强化学习 （DRL） 方法，利用参与者-批评者架构来训练使用指针网络结构和注意力机制建模的学习网络。还提出了一种搜索策略来优化学习参数并确定最终的设计策略。与现有的两种方法相比，所提出的算法在能源效率和累积接受率方面表现出更好的性能。具体来说，通过所提出的方法实现的 EE KPI 提高到 69.7%。