面向密集网络中移动用户链路选择的轻量级推荐框架

Ji Wang, Xiaomin Zhu, Weidong Bao, Guanlin Wu
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摘要

随着移动设备的普及和通信技术的发展,移动设备已经渗透到我们日常生活的方方面面。然而,在密集网络中,大量移动设备试图同时接入网络,移动设备之间的严重干扰可能会导致无线通信质量的显著下降。如何在这种情况下提高个人的体验是一个关键而又悬而未决的问题。本文根据移动设备用户的使用模式以及大多数无线通信系统的特点,提出了一种针对不同移动设备用户提供上行/下行选择推荐的框架,以提高其效用。框架的设计首先将问题表述为链接选择游戏。分析表明,该对策可归类为一个保证纳什均衡的广义有序势对策。然后,我们设计了一个分布式链路选择算法来生成博弈的纳什均衡。为了适应密集网络的特点和移动设备的容量限制,该算法的设计具有轻量化的特点,不需要每个移动设备用户知道其他人的当前选择。同时考虑了信息收集不完全的概率。大量的实验证明了该框架的有效性和优越性。实验结果表明,全球平均效用增长率达到20%以上,约70%的移动设备用户可以从我们的框架中受益。
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A Lightweight Recommendation Framework for Mobile User’s Link Selection in Dense Network
With the proliferation of mobile devices and the development of communication technology, mobile devices have permeated every aspect of our daily lives. However, in dense network where large crowd of mobile devices try to access to the network simultaneously, the severe interference between mobile devices may incur a remarkable deterioration of the wireless communication quality. How to improve individual's experience in such scenario is a critical yet open problem. Inspired by the mobile device users' usage pattern as well as the characteristic of most wireless communication systems, we propose a framework offering uplink/downlink selection recommendation to different mobile device users to enhance their utility in this paper. The design of the framework starts with formulating the problem as a link selection game. Analysis shows that the game can be categorized as a generalized ordinal potential game whose Nash Equilibrium is guaranteed. We then devise a distributed link selection algorithm to generate a Nash Equilibrium of the game. To accommodate to the characteristic of dense network and the capacity limitation of mobile device, the design of the algorithm shows a light-weight property and does not require each mobile device user to know others' current selection. The probability of incomplete information gathering is also considered. Extensive experiments are conducted to demonstrate the effectiveness and superiority of the proposed framework. Experimental results show that the global average utility increase rate reaches above 20%, and about 70% mobile device users can benefit from using our framework.
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