Bayesian-based channel quality estimation method for LoRaWAN with unpredictable interference
Bayesian-based channel quality estimation method for LoRaWAN with unpredictable interference
复制标题
基于贝叶斯的LoRaWAN不可预测干扰信道质量估计方法
DOI：
10.1109/globecom42002.2020.9322136
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Murata Masayuki
中科院分区：
文献类型：
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作者：
Kominami Daichi;Hasegawa Yohei;Nogami Kosuke;Shimonishi Hideyuki;Murata Masayuki
“物联网”已经成为一个通用术语，低功耗广域（LPWA）技术作为其基本技术之一备受关注。LPWA在不消耗太多能量的情况下实现广域通信，允许各种数据传感和采集应用。LoRa是一种使用免许可频段的LPWA通信技术。由于可以使用LoRa构建自我管理的网络，因此许多基于LoRa的服务将分散在同一区域，而无需总管理员。因此，由于意外的无线电干扰，LoRa的通信性能可能会下降。不幸的是，包括LoRa在内的许多LPWA技术具有较低的数据速率，这使得难以收集足够的控制信息来避免通信性能的下降。本文提出了一种利用先验分布的贝叶斯更新逐次估计来估计网络拥塞状态的方法。计算机仿真表明，该方法可以在积累少量控制信息的情况下估计网络状态。
The “Internet of things” has become a common term, and low-power wide-area (LPWA) technology is attracting much attention as one of its elemental technologies. LPWA achieves wide-area communication without consuming much energy, allowing various data sensing and gathering applications. LoRa is an LPWA communication technology that uses unlicensed bands. Because it is possible to build a self-managed network with LoRa, many LoRa-based services will be scattered in the same area without an overall administrator. As a result, the communication performance of LoRa may degrade due to unintended radio interference. Unfortunately, many LPWA techniques, including LoRa, have low data rates, making it difficult to gather sufficient control information to avoid such degradation of communication performance. In this paper, we propose a method for estimating network congestion states through successive estimation using Bayesian updates of prior distributions. Computer simulations show the network state can be estimated by our proposed method with accumulating a little control information.
MPEG-DASH 的贝叶斯吸引子模型的速率自适应
DOI：
10.1109/ccwc.2019.8666543
发表时间：
2019
期刊：
2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)
影响因子：
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作者：
Masayoshi Iwamoto;Tatsuya Otoshi;D. Kominami;M. Murata
通讯作者：
M. Murata