【作者】 李小娜；

【导师】 赵菊敏； 王乐；

【作者基本信息】 太原理工大学 ， 通信工程（含宽带网络、移动通信等）（专业学位）， 2024， 硕士

【摘要】 物联网作为新时代信息技术重要组成部分,展现出卓越的智能互联特性,催生了众多新兴行业,推动人类社会迈向更加智能、高效、便捷的世界。反向散射网络作为连接海量物联网传感设备的关键通信技术,已广泛应用于智慧物流、智慧仓储、智慧医疗等领域。射频识别技术(RFID)自身无需配备电源设备供电,从射频信号中捕获能量实现数据传输和无线通信,由于其成本低廉、可标记性强且维护便捷,已成为重要的“无源感知”媒介,是反向散射的关键技术之一。随着RFID技术的不断普及和应用场景的拓展,反向散射设备的广泛部署导致传输数据量和系统计算量显著增加,通信环境日趋复杂。多标签环境中存在严重的时隙碰撞和信道质量多变等问题,不可避免地导致标签读取率减小,系统吞吐量降低。为了高效的读取标签实现及时的响应和准确的分析,本文将特定场景中具有相同运动行为的目标标签作为研究对象,识别并提取标签间共同特征进行标签分组,同时,为了提高标签组吞吐量,提出了基于标签组的速率自适应方法GFRA,实现了通信系统的优化。本文主要工作如下:首先,本文提出一种基于标签特征识别的动态标签分组方法,其中具有相同运动行为的标签其信号接收强度RSSI(Received Signal Indicator,RSSI)波形变化具有高度相似性,专注于波形序列形状变化使用Frechet算法提取波形特征,结合相似传递性设计标签分组方法,并在三种不同干扰下验证标签分组准确率。对已识别的标签组进行选择通信时,基于协议中Select命令选择一组标签的工作原理实现对标签组的选择性通信,提高标签读取率。其次,基于标签分组提出了一种速率自适应方法,主要包括两个方面:触发器设计和速率选择算法优化设计。为精准把握标签速率切换时机,对RSSI作方差处理并结合相位滞后性特点设计二级触发机制,降低了误报率,提高了触发器精度。速率选择算法优化中将RSSI方差和丢包率作为输入指标,精准描述信道质量变化,采用模糊算法对指标模糊化,并依据实验经验设计合理模糊规则,为标签组选择最优传输速率,以提高标签平均吞吐量。最后,在三种不同的干扰场景下,使用阅读器和多个商用标签进行重复实验,收集多组数据并进行深入分析和评估。结果表明本文提出的方法可有效改善标签读取率低的问题,噪声干扰环境中,标签读取率是原有方法的2.1×,行人干扰和阻挡干扰中分别是1.7×和1.35×,且与其他两种算法对比,GFRA算法显著提升了通信吞吐量。更多还原

【Abstract】 The Internet of Things(IoT),as an important component of the new era of information technology,demonstrates outstanding intelligent interconnection characteristics,giving rise to numerous emerging industries and driving human society towards a more intelligent,efficient,and convenient world.Reverse Backscatter Networks,as a key communication technology connecting massive Io T sensing devices,have been widely applied in fields such as smart logistics,smart warehousing,and smart healthcare.Radio Frequency Identification(RFID)technology,which does not require power-supply equipment,captures energy from radio frequency signals to achieve data transmission and wireless communication.Due to its low cost,strong markability,and easy maintenance,RFID has become an important"passive sensing"medium and one of the key technologies for reverse backscatter.With the continuous popularization of RFID technology and the expansion of application scenarios,the widespread deployment of reverse backscatter devices has significantly increased the amount of transmitted data and system computation,leading to increasingly complex communication environments.In multi-tag environments,there are serious issues such as slot collisions and variable channel quality,inevitably resulting in reduced tag read rates and decreased system throughput.In order to efficiently read tags to achieve timely responses and accurate analysis,this thesis focuses on tags with similar motion behaviors in specific scenarios as research objects.It proposes a method for identifying and extracting common features among tags for tag grouping based on tag characteristics.Furthermore,to improve tag group throughput,a rate-adaptive method based on tag groups(GFRA)is proposed to optimize communication systems.The main contributions of this thesis are as follows:Firstly,this thesis proposes a dynamic tag grouping method based on tag characteristics recognition.Tags with similar motion behaviors have highly similar Received Signal Indicator(RSSI)waveform changes.By focusing on the shape changes of waveform sequences and using the Frechet algorithm to extract waveform features,a tag grouping method is designed based on similarity transitivity,which is validated under three different interference scenarios to verify the accuracy of tag grouping.When selecting communication for identified tag groups,selective communication for tag groups is achieved based on the working principle of the Select command in the protocol to improve tag read rates.Secondly,based on tag grouping,a rate adaptive method is proposed,which mainly includes two aspects:trigger design and optimization of rate selection algorithm.To accurately grasp the timing of tag rate switching,a two-level trigger mechanism is designed by processing the variance of RSSI and combining the characteristics of phase lag,which reduces the false alarm rate and improves the accuracy of the trigger.In the optimization of rate selection algorithm,RSSI variance and packet loss rate are taken as input indicators to accurately describe the change of channel quality.Fuzzy algorithm is adopted to fuzzify the indicators,and reasonable fuzzy rules are designed based on experimental experience to select the optimal transmission rate for tag groups,aiming to improve the average throughput of tags.Finally,under three different interference scenarios,repeated experiments are conducted using readers and multiple commercial tags,and multiple sets of data are collected for in-depth analysis and evaluation.The results show that the proposed method can effectively improve the problem of low tag read rates.In noise interference environments,the tag read rate is improved by 2.1×,while in pedestrian interference and blockage interference,it is improved by 1.7×and 1.35×respectively.Furthermore,compared with the other two algorithms,the GFRA algorithm significantly enhanced the communication throughput.更多还原