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首页> 外文会议>International Conference on Digital Information and Communication Technology and it's Applications >KPI signal analysis of the custom behavior in Kenting Spring Scream festival hot zone
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KPI signal analysis of the custom behavior in Kenting Spring Scream festival hot zone

机译：垦丁春节狂欢热点地区习惯行为的KPI信号分析。

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In this study, the speed test software Nemo Handy installed on an engineering-mode mobile phone was used to measure the key performance indicators (KPI) of circuit-switched (CS) and packet-switched (PS) signal parameters generated from Node B located around the venue of the Spring Scream festival held in Kenting, Taiwan between April 3, 2013 and April 7, 2013. The measured signal parameters were then processed using the postprocessing software of Nemo Outdoor and Nemo Analyze to examine the quality of the communication signals. The items of the KPI parameters that were analyzed included slope, standard deviation (SD), drop call, UL throughput, DL throughput, and web open time (web time). The resulting data were then compared with the internal data generated at the system end of the switching device to understand the behavior of mobile phone users in various environments. Based on the varying behavioral models, the parameters of the node B were adjusted to optimize the performance of the node B. During the mobility test, 1 drop call occurred out of the 55 call attempts, yielding a DCR of 1.818%. According to the Splunk software at the system end, the DCRs during circuit switching and packet switching remained below 2%. The connect success rate of the radio resource control (RRC) and radio access bearer (RAB) was maintained above 98% and 97%, respectively. This high success rate was sustained despite using the mobile phone during peak hours, indicating that the resources available at the system end are sufficient.
机译：在这项研究中，使用安装在工程模式手机上的速度测试软件Nemo Handy来测量从节点B生成的电路交换（CS）和分组交换（PS）信号参数的关键性能指标（KPI）在2013年4月3日至2013年4月7日在台湾垦丁举行的春季尖叫节的地点附近。然后，使用Nemo Outdoor和Nemo Analyze的后处理软件对测得的信号参数进行处理，以检查通信信号的质量。分析的KPI参数的项目包括斜率，标准偏差（SD），掉线，UL吞吐量，DL吞吐量和Web开放时间（Web时间）。然后将所得数据与在交换设备的系统端生成的内部数据进行比较，以了解移动电话用户在各种环境中的行为。根据变化的行为模型，调整节点B的参数以优化节点B的性能。在移动性测试期间，55次呼叫尝试中发生1次掉话，产生的DCR为1.818％。根据系统端的Splunk软件，电路交换和数据包交换期间的DCR保持在2％以下。无线资源控制（RRC）和无线接入承载（RAB）的连接成功率分别保持在98％和97％以上。尽管在高峰时段使用了移动电话，仍然保持了如此高的成功率，这表明系统端可用的资源是足够的。

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