Suppr超能文献

密集毫微微蜂窝网络中基于预测的关联控制方案。

Prediction-based association control scheme in dense femtocell networks.

作者信息

Sung Nak Woon, Pham Ngoc-Thai, Huynh Thong, Hwang Won-Joo, You Ilsun, Choo Kim-Kwang Raymond

机构信息

Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea.

Department of Information and Communications Engineering, Inje University, Gimhae 50834, Republic of Korea.

出版信息

PLoS One. 2017 Mar 22;12(3):e0174220. doi: 10.1371/journal.pone.0174220. eCollection 2017.

Abstract

The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system's effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective.

摘要

大量毫微微蜂窝基站的部署使我们能够以低成本方式扩展覆盖范围并有效利用资源。然而,毫微微蜂窝网络的小区尺寸较小,可能导致移动用户频繁切换,从而导致吞吐量下降。因此,在本文中,我们提出了预测关联控制方案,以提高系统的有效吞吐量。我们的设计侧重于在不影响吞吐量的情况下降低切换频率。所提出的方案确定对网络吞吐量贡献最大且适合分布式实现的切换决策。从切换频率和网络吞吐量的角度来看,仿真结果表明与现有方法相比有显著提升。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77f3/5362202/986182cf6c81/pone.0174220.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验