With recent advancements in the sixth generation (6G) communication technologies, more vertical industries have encountered diverse network services. How to reduce energy consumption is critical to meet the expectation of the quality of diverse network services. In particular, the number of base stations in 6G is huge with coupled adjustable network parameters. However, the problem is complex with multiple network objectives and parameters. Network intents are difficult to map to individual network elements and require enhanced automation capabilities. In this paper, we present a network intent decomposition and optimization mechanism in an energy-aware radio access network scenario. By characterizing the intent ontology with a standard template, we present a generic network intent representation framework. Then we propose a novel intent modeling method using Knowledge Acquisition in automated Specification language, which can model the network ontology. To clarify the number and types of network objectives and energy-saving operations, we develop a Softgoal Interdependency Graph-based network intent decomposition model, and thus, a network intent decomposition algorithm is presented. Simulation results demonstrate that the proposed algorithm outperforms without conflict analysis in intent decomposition time. Moreover, we design a deep Q-network-assisted intent optimization scheme to validate the performance gain.

翻译：随着第六代（6G）通信技术的进步，众多垂直行业面临多样化的网络服务需求。如何降低能耗以满足多样化网络服务质量预期至关重要。特别地，6G中基站数量庞大且可调网络参数相互耦合，而该问题涉及多网络目标与参数，复杂度极高。网络意图难以映射至单个网络单元，亟需增强的自动化能力。本文提出一种面向能量感知无线接入网络场景的网络意图分解与优化机制：通过标准模板定义意图本体，构建通用网络意图表征框架；进而提出基于知识获取自动化规范语言的创新意图建模方法，实现网络本体建模。为明确网络目标与节能操作的数量及类型，我们开发了基于软目标依赖图的网络意图分解模型，并据此给出网络意图分解算法。仿真结果表明，相较于无冲突分析方法，所提算法在意图分解时间上表现更优。此外，我们设计了深度Q网络辅助的意图优化方案以验证性能增益。