基于学习的状态空间搜索技术的VoIP呼叫优化
VoIP Call Optimization in Diverse Network Scenarios Using Learning Based State-Space Search Technique
摘要：
基于VoIP的呼叫在延迟、抖动、丢失、MOS和R因子方面具有严格的QoS要求。为满足这些要求而实施的各种QoS机制必须在不同的网络场景下自适应，并按适当的顺序应用，否则它们可能会相互冲突。本文的目的是解决问题的自适应QoS维护和顺序执行可用的QoS实现机制相对于VoIP在不同的网络条件下。本文将这一问题推广为状态空间问题并加以求解，首先将QoS优化问题映射到状态空间域，采用增量式启发式搜索进行求解。我们实现了所提出的算法在各种网络和用户的情况下，在VoIP测试床的QoS增强。然后，学习策略的知识库的细化，以提高性能的呼叫质量随着时间的推移。最后，我们讨论了我们的方法的优势和独特性。
英文摘要：
A VoIP based call has stringent QoS requirements with respect to delay, jitter, loss, MOS and R-Factor. Various QoS mechanisms implemented to satisfy these requirements must be adaptive under diverse network scenarios and applied in proper sequence, otherwise they may conflict with each other. The objective of this paper is to address the problem of adaptive QoS maintenance and sequential execution of available QoS implementation mechanisms with respect to VoIP under varying network conditions. In this paper, we generalize this problem as state-space problem and solve it. Firstly, we map the problem of QoS optimization into state-space domain and apply incremental heuristic search. We implement the proposed algorithm under various network and user scenarios in a VoIP test-bed for QoS enhancement. Then learning strategy is implemented for refinement of knowledge base to improve the performance of call quality over time. Finally, we discuss the advantages and uniqueness of our approach.
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。
