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Joint Spreading Factor and Coding Rate Assignment in LoRaWAN Networks

机译:LoRaWAN网络中的联合扩展因子和编码速率分配

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LoRaWAN is a fast developing Low Power Wide Area Network technology that is predicted to support a large segment of the billions of estimated Internet of Things devices. It promises ubiquitous connectivity keeping network structures and management simple. One open key challenge is how to effectively assign a spreading factor so as to support a large number of end devices by a single gateway within a LoRaWAN cell. This paper confirms that if more clever spreading factor allocation than the conventionally deployed Adaptive Data Rate is considered, network performance will improve significantly. The proposed heuristics are based on two key features; first the fact that each subset of end devices allocated a certain spreading factor represents a spreading factor tier, each signify a logical pure Aloha network with a different packet length. Second, the datum to control coding rate transmission parameter which in turns control coverage sensitivities and thus system load in each spreading factor tier. Two proposed algorithms, namely sensitivitySF and assignmentSF, attempt to maximize the throughput of each spreading factor tier and thus enhance overall network performance. Simulation results show that the proposed algorithms can considerably improve network performance when compared to the basic adaptive data rate strategy, and particularly the assignmentSF algorithm consistently guarantees a high success rate for any generated traffic load.
机译:LoRaWAN是一项快速发展的低功耗广域网技术,预计将支持估计的数十亿物联网设备中的很大一部分。它保证了无处不在的连接性,使网络结构和管理变得简单。一个开放式关键挑战是如何有效地分配扩频因子,以便通过LoRaWAN小区内的单个网关支持大量终端设备。本文证实,如果考虑比传统部署的“自适应数据速率”更聪明的扩展因子分配,网络性能将大大提高。提议的启发式方法基于两个关键特征;首先,分配有一定扩展因子的终端设备的每个子集代表一个扩展因子层,每个都表示具有不同数据包长度的逻辑纯Aloha网络。第二,控制编码率传输参数的数据,继而控制覆盖范围的敏感度,进而控制每个扩频因子层中的系统负载。提出的两种算法,即灵敏度SF和分配SF,试图最大化每个扩频因子层的吞吐量,从而提高整体网络性能。仿真结果表明,与基本的自适应数据速率策略相比,所提出的算法可以显着提高网络性能,尤其是assignmentSF算法可以始终保证任何生成的流量负载的高成功率。

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