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A MDP-Based Network Selection Scheme in 5G Ultra-Dense Network

机译：基于MDP的网络选择方案5G超密集网络

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摘要

With the rapid development of the mobile Internet and the Internet of Things, the number of mobile communication services has grown rapidly. When multiple different types of networks cover the same region, it is important to decide which one users connect to, known as the network selection problem. In this paper, we explore the optimal network selection problem in 5G ultra-dense network. We consider several different types of transmission data such as session, media, background and interactive, which conclude different QoS requirements. And then, we formulate the network selection problem as an MDP model in ultra-dense system, and propose NS-MDP algorithm which aims to obtain best target network by calculating the benefits of the utility function. NS-MDP algorithm takes into account user data requirements, current system status, and network load conditions. Comparison experiments with Best-Rate, Random and Greedy_AHP strategies, show that NS-MDP algorithm's average throughput is 8.6%, 17.8% and 20.5% higher than them, and NS-MDP can reduce blocking rate by 33.3%, 17.6%, and 37.7%.
机译：随着移动互联网和物联网的快速发展，移动通信服务的数量已迅速增长。当多种不同类型的网络覆盖相同的区域时，重要的是决定一个用户连接，称为网络选择问题。在本文中，我们在5G超密集网络中探讨了最佳网络选择问题。我们考虑了几种不同类型的传输数据，如会话，媒体，背景和交互式，其结束了不同的QoS要求。然后，我们将网络选择问题作为超密集系统中的MDP模型，提出了NS-MDP算法，该算法旨在通过计算实用程序功能的好处来获得最佳目标网络。 NS-MDP算法考虑了用户数据要求，当前系统状态和网络负载条件。与百思速率，随机和Greedy_AHP策略比较实验，证明NS-MDP算法的平均吞吐量为8.6％，17.8％和20.5％，比他们高，和NS-MDP可以通过33.3％，17.6％和37.7降低阻塞率％。

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