i5GAccess:基于纳什q学习的5G异构网络多业务边缘用户接入

Anqi Zhu, Songtao Guo, Mingfang Ma
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引用次数: 0

摘要

在异构无线网络中,如何实现高效的网络选择策略以满足大量边缘用户和新型5G业务的需求仍然是一个重大挑战。本文将边缘用户的网络选择问题表述为离散时间马尔可夫模型,提出了一种基于纳什q学习的多智能体系统智能网络接入算法MAQNS。为了使多智能体系统的长期性能最大化,我们考虑了不同类型网络间网络选择策略的联合优化。同时,我们使用层次分析法(AHP)和灰色关联分析法(GRA)来表征用户对网络的偏好。实验结果表明,与现有的网络选择算法相比,所提出的MAQNS在系统吞吐量、用户阻塞概率、平均能量效率和平均延迟方面具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
i5GAccess: Nash Q-learning Based Multi-Service Edge Users Access in 5G Heterogeneous Networks
In the heterogeneous wireless networks, it remains a significant challenge to achieve an efficient network selection strategy to satisfy the demands of a massive number of edge users and novel 5G services. In this paper, we formulate the network selection problem for edge users as a discrete-time Markov model, and propose a Nash Q-learning based intelligent network access algorithm for multi-agent system, named MAQNS. We consider the joint optimization of network selection strategies among different types of networks, aiming at maximizing the long-term performance of multi-agent system. Meanwhile, we use Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to characterize the user preferences for networks. Experimental results show that comparing to the existing network selection algorithms, the proposed MAQNS has better performance in terms of system throughput, user blocking probability, average energy efficiency and average delay.
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