意图驱动自治网络中的直接冲突解决
Direct-Conflict Resolution in Intent-Driven Autonomous Networks
AI总结：
本文针对意图驱动自治网络中多意图并发导致的冲突问题，引入WNBS、KSBS和SEBS等策略扩展NBS方法，通过仿真比较天线倾角解，并依据Jain公平性指数确定KSBS为最公平方案。
摘要：
随着网络系统的演进，对自动化工具以促进高效管理和配置的需求日益增长。本文探讨了基于意图的网络（Intent-Based Network，IBN）管理中的冲突解决问题，这是一种创新的方法，有望实现有效的网络管理，尤其是在无线接入域内。然而，当多个意图同时运行时，可能会出现冲突，这构成了当前文献中仍未得到充分解决的一个重要问题。为应对这一挑战，我们的研究将冲突解决策略的范围从既有的纳什讨价还价解（Nash Bargaining Solution，NBS）扩展至加权纳什讨价还价解（Weighted Nash Bargaining Solution，WNBS）、卡莱-斯莫罗丁斯基讨价还价解（Kalai-Smorodinsky Bargaining Solution，KSBS）和香农熵讨价还价解（Shannon Entropy Bargaining Solution，SEBS）。这些方法被用于寻找最优参数值，旨在确保冲突解决中的公平性。通过仿真，证明了每种方法分别产生不同的天线倾角值作为各自的解。最终，基于Jain公平性指数，KSBS被确定为给定条件下最公平的方法。
英文摘要：
As network systems evolve, there is an escalating demand for automated tools to facilitate efficient management and configuration. This paper explores conflict resolution in Intent-Based Network (IBN) management, an innovative approach that holds promise for effective network administration, especially within radio access domain. Nevertheless, when multiple intents are in operation concurrently, conflicts may emerge, presenting a significant issue that remains under-addressed in the current literature. In response to this challenge, our research expands the range of conflict resolution strategies beyond the established Nash Bargaining Solution (NBS), to incorporate the Weighted Nash Bargaining Solution (WNBS), the Kalai-Smorodinsky Bargaining Solution (KSBS), and the Shannon Entropy Bargaining Solution (SEBS). These methods are employed with the objective to identify optimal parameter values, aiming to ensure fairness in conflict resolution. Through simulations, it is demonstrated that distinct antenna tilt values are yielded as the respective solutions for each method. Ultimately, based on Jain Fairness Index, the KSBS is identified as the most equitable method under the given conditions.
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。
