PHY/MAC Uplink Performance of Class A LoRa Networks

Research output: Contribution to journalArticle

Abstract

Recently, Low Power Wide Area Networks (LPWANs) have attracted great interest due to the need of connecting more and more devices to the so-called Internet of Things (IoT). LoRa networks are LPWANs that allow a long-range radio connection of multiple devices operating in non-licensed bands. In this work, we characterize the performance of LoRa’s Uplink communications where both physical layer (PHY) and medium access control (MAC) are taken into account. Motivated by recent works that consider the possibility of decoding multiple frames at the same time, we characterize the performance of the PHY-layer through the probability of decoding multiple frames that were transmitted with the same spreading factor. The MAC performance is evaluated by considering that the inter-arrival time of the frames generated by each LoRa device is exponentially distributed. A LoRaWAN operating scenario is considered, where the transmissions of LoRa Class A devices are influenced by path loss, shadowing and Rayleigh fading. Numerical results obtained with the modeling methodology are compared with simulation results, and the validation of the proposed model is discussed for different traffic load levels, different PHY-layer conditions, and different capture thresholds. The contribution of this work is primarily focused on studying the average number of decoded LoRa frames for the different capture conditions, being a general model when compared to the works published so far. Moreover, given the different research initiatives to develop innovative multi-capture LoRa schemes, we believe that the proposed model is particularly useful to foresee LoRa’s PHY/MAC performance in such innovative scenarios.
Original languageEnglish
Article number9000517
JournalIEEE Internet of Things Journal
DOIs
Publication statusE-pub ahead of print - 17 Feb 2020

Keywords

  • LoRa Networks
  • PHY/MAC Modeling
  • Performance Evaluation

Fingerprint Dive into the research topics of 'PHY/MAC Uplink Performance of Class A LoRa Networks'. Together they form a unique fingerprint.

Cite this