TY - JOUR
T1 - PHY/MAC Uplink Performance of LoRa Class A Networks
AU - Furtado, Antonio
AU - Pacheco, João
AU - Oliveira, Rodolfo
N1 - projects CoSHARE (LISBOA-01-0145-FEDER-0307095; PTDC/EEI-TEL/30709/2017);
InfoCent-IoT (POCI-01-0145-FEDER-030433);
UID/EEA/50008/2019.
PY - 2020/7
Y1 - 2020/7
N2 - 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 nonlicensed bands. In this article, 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 interarrival 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. The 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 article 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 multicapture LoRa schemes, we believe that the proposed model is particularly useful to foresee LoRa's PHY/MAC performance in such innovative scenarios.
AB - 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 nonlicensed bands. In this article, 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 interarrival 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. The 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 article 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 multicapture LoRa schemes, we believe that the proposed model is particularly useful to foresee LoRa's PHY/MAC performance in such innovative scenarios.
KW - LoRa networks
KW - performance evaluation
KW - physical layer (PHY)/medium access control (MAC) modeling
UR - http://www.scopus.com/inward/record.url?scp=85088573300&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2020.2974429
DO - 10.1109/JIOT.2020.2974429
M3 - Review article
AN - SCOPUS:85088573300
SN - 2327-4662
VL - 7
SP - 6528
EP - 6538
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 7
M1 - 9000517
ER -