.png){kind=icon}
返回
Adaptive Multi-Channels Allocation in LoRa Networks
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3040765.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we consider an IoT dedicated network corresponding to a non licensed LoRa Low Power Wide Area Network. The LoRa network operates in the unlicensed 868 MHz band within a total bandwidth of 1 MHz divided into 8 orthogonal channels of 125 kHz each. Despite the high level of interference, this network offers long range communications in the order of 2 to 5 km in urban areas and 10 to 30 km in rural areas. To efficiently mitigate this high level of interference, LoRa network essentially relies on a Chirp Spread Spectrum (CSS) modulation and on repetition diversity mechanisms. The LoRa CSS modulation spreads the signal within a band of 125 kHz using 6 possible spreading factors (from 7 to 12) to target data rates (starting from 5 kbps for the closest node to 300 bps for the furthest ones). The repetition diversity mechanisms enable the data recovery when the transmission is subject to bad channel conditions or/and high interference levels. Although the CSS modulation protects edge-cell's devices from the high level of interference induced by nodes in the proximity of the gateway, it fails to protect nodes at the edge of a given SF region and several trials are required to recover the packet. In this paper, we propose an adaptive multi-channels allocation policy that attributes multiple adjacent channels of 125 kHz for nodes situated at the edge of SF zones. We study the impact of this adaptive sub-band allocation on the gateways' intensities, the rate distribution and the power consumption. Our results are based on a statistical characterization of the interference in the network as well as the outage probability in a typical cell.
Keyword:
Logic gates
Interference
Chirp
Bandwidth
Modulation
Cascading style sheets
Resource management
Low Power Wide Area Network (LPWAN)
LoRa network
Chirp Spread Spectrum modulation
Internet of Things
stochastic geometry
spatial Poisson Point Process
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN基于LoRa和LoRaWAN的传感器节点能耗模型.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
A Study of LoRa: Long Range & Low Power Networks for the Internet of ThingsLoRa的研究: 物联网的长距离和低功耗网络SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容