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一种自适应的太赫兹无线个域网高效定向MAC协议
引用本文:任智,吕昱辉,徐兆坤,邹明芮,田洁丽.一种自适应的太赫兹无线个域网高效定向MAC协议[J].电子与信息学报,2019,41(1):99-106.
作者姓名:任智  吕昱辉  徐兆坤  邹明芮  田洁丽
作者单位:重庆邮电大学移动通信技术重庆市重点实验室 重庆 400065;中国工程物理研究院激光聚变研究中心 成都 610000
基金项目:国家自然科学基金;重庆市基础与前沿研究计划项目
摘    要:针对现有太赫兹无线个域网定向MAC协议存在的波束训练开销和入网时延偏大以及Beacon, S-CAP时段时隙利用不足问题,该文提出一种自适应的定向MAC协议——AD-MAC,自适应地在静态场景下采用全网协同波束训练,在动态场景下节点基于历史信息快速回复波束训练帧,同时使用反向监听策略减小同扇区节点的帧碰撞概率,并且通过时隙复用在Beacon和S-CAP时段并行发送控制帧和数据帧。理论分析表明了AD-MAC协议的有效性,仿真结果显示:相较于ENLBT-MAC等典型协议,AD-MAC在静态场景下的波束训练开销和节点平均入网时延分别降低了约21.84%和22.70%,在动态场景下上述二指标则分别减小了约18.7%和13.07%。

关 键 词:无线个域网    太赫兹    MAC协议    定向
收稿时间:2018-04-02

An Adaptive Directional MAC Protocol for Terahertz Wireless Personal Networks
Zhi REN,Yuhui Lü,Zhaokun XU,Mingrui ZOU,Jieli TIAN.An Adaptive Directional MAC Protocol for Terahertz Wireless Personal Networks[J].Journal of Electronics & Information Technology,2019,41(1):99-106.
Authors:Zhi REN  Yuhui Lü  Zhaokun XU  Mingrui ZOU  Jieli TIAN
Affiliation:1.Chongqing Key Laboratory of Mobile Communication Technology, Chongqing University of Post & Telecommunications, Chongqing 400065, China2.Research Center of Laser Fusion, Chinese Academy of Engineering Physics, Chengdu 610000, China
Abstract:To reduce the beamforming training cost and network delay, make the best of Beacon and S-CAP sub-period in the existing Terahertz Wireless Personal Access Network (TWPAN) directional MAC protocols, an Adaptive Directional MAC (AD-MAC) protocol for TWPAN is proposed. AD-MAC adaptively uses the entire network cooperative beam training in a static scenario, and makes network nodes quickly respond to beam training frames based on historical information in a dynamic scenario. The reverse listening strategy is used to reduce the collision probability of same sector nodes. The control frame and data frame are transmitted simultaneously in the Beacon and S-CAP slot using time-slot reuse. Theoretical analysis verifies the effectiveness of AD-MAC. Also, simulation results show that, comparing with ENLBT-MAC, AD-MAC reduces about 21.84% of beamforming training cost and 22.70% of the average network delay in static scene, and reduces about 18.7% of beamforming training cost and 13.07% of the average network delay in dynamic scene.
Keywords:
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