Impact of Spreading Factor Imperfect Orthogonality in LoRa Communications

IEEE Wireless Commun. Lett.

et al. 2019

137

LoRa is one of the promising techniques for enabling Low Power Wide Area Networks (LPWANs) for future Internet-of-Things (IoT) devices. Although LoRa allows flexible adaptations of coverage and data rates, it is subject to intrinsic types of interferences: co-SF interferences where end-devices with the same Spreading Factors (SFs) are subject to collisions, and inter-SF interferences where end-devices with different SFs experience collisions. Most current works have considered perfect orthogonality among different SFs. In this work, we provide a theoretical analysis of the achievable LoRa throughput in uplink, where the capture conditions specific to LoRa are included. Results show the accuracy of our analysis despite approximations, and the throughput losses from imperfect SF orthogonality, under different SF allocations. Our analysis will enable the design of specific SF allocation mechanisms, in view of further throughput enhancements. 1

mentioning

confidence: 99%

LoRa Throughput Analysis With Imperfect Spreading Factor Orthogonality

Waret

IEEE Wireless Commun. Lett.

et al. 2019

137

“…CANCELLATION OF LORA SIGNALS LoRa gateways are able to decode superposed LoRa signals as long as they are sent on different SFs. Notice however that some researchers have shown that signals on different SFs are not completely orthogonal [11], [12].…”

Section: B Loramentioning

confidence: 96%

Decoding Superposed LoRa Signals

Rachkidy

2018 IEEE 43rd Conference on Local Computer Networks (LCN)

2018

Long-range low-power wireless communications, such as LoRa, are used in many IoT and environmental monitoring applications. They typically increase the communication range to several kilometers, at the cost of reducing the bitrate to a few bits per seconds. Collisions further reduce the performance of these communications. In this paper, we propose two algorithms to decode colliding signals: one algorithm requires the transmitters to be slightly desynchronized, and the other requires the transmitters to be synchronized. To do so, we use the timing information to match the correct symbols to the correct transmitters. We show that our algorithms are able to significantly improve the overall throughput of LoRa.

“…LoRa gateways are able to decode superposed LoRa signals as long as they are sent on different channels or on different SFs. Notice however that some researchers have shown that signals on different SFs are not completely orthogonal [17], [18], [19].…”

Section: Proposed Superposed Lora Signal Decodingmentioning

confidence: 97%

Collision Resolution Protocol for Delay and Energy Efficient LoRa Networks

Rachkidy

IEEE Trans. on Green Commun. Netw.

2019

Future 5G and Internet of Things (IoT) applications will heavily rely on long-range communication technologies such as low-power wireless area networks (LPWANs). In particular, LoRaWAN built on LoRa physical layer is gathering increasing interests, both from academia and industries, for enabling low-cost energy efficient IoT wireless sensor networks for, e.g., environmental monitoring over wide areas. While its communication range may go up to 20 kilometers, the achievable bit rates in LoRaWAN are limited to a few kilobits per second. In the event of collisions, the perceived rate is further reduced due to packet loss and retransmissions. Firstly, to alleviate the harmful impacts of collisions, we propose a decoding algorithm that enables to resolve several superposed LoRa signals. Our proposed method exploits the slight desynchronization of superposed signals and specific features of LoRa physical layer. Secondly, we design a full MAC protocol enabling collision resolution. The simulation results demonstrate that the proposed method outperforms conventional LoRaWAN jointly in terms of system throughput, energy efficiency as well as delay. These results show that our scheme is well suited for 5G and IoT systems, as one of their major goals is to provide the best trade-off among these performance objectives.