PCube: Scaling LoRa Concurrent Transmissions with Reception Diversities

Xia, Xianjin; Hou, Ningning; Zheng, Yuanqing; Gu, Tao

doi:10.1145/3545571

Cited by 11 publications

(5 citation statements)

References 63 publications

(39 reference statements)

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“…− LoRa protocol stack: LoRa is an up-and-coming LPWAN technology, therefore many people have been working on improving its protocol stack, particularly its PHY and MAC layers. The capabilities and promise of the LoRa PHY layer have been shown through applications such as collision disambiguation [51], sensing [52], and backscatter [53]. Adopting deep learning networks [54] can overcome some inherent limitations of LoRa conventional PHY decoding while ensuring the benefits of its weak and collision decoding ability by utilising LoRa PHY packet structure and chirp features in the time and frequency domains, exploiting spatial [55] diversity gain.…”

Section: Future Directionsmentioning

confidence: 99%

Long range technology for internet of things: review, challenges, and future directions

A. Al-Shareeda,

Abdullah Alsadhan,

H. Qasim

et al. 2023

Bulletin EEI

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New networking issues are presented by the increasing need for a wide variety of applications, which has spurred the creation of a new internet of things (IoT) paradigm, such as long range (LoRa). The LoRa protocol uses a patented kind of spread spectrum modulation to provide low-power, long-range communication. In this paper, we provide a comprehensive review of LoRa-IoT in terms of IoT applications, LoRa class, security and privacy requirements, and the evolution of LoRa technology. This review analysis and compares long range wide area network (LoRaWAN) to wireless technology (e.g., Bluetooth, LoRa, 5G, Sigfox, long term evolution-M (LTE-M), Wi-Fi, Z-wave, Zigbee) and provides a list of environment simulators (e.g., OMNeT++, MATLAB, ns-3, SimPy) to carry out experiment for LoRa-IoT. Finally, this review does not only review literature recently studied for LoRa-IoT but also discusses challenges and future directions.

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Section: Future Directionsmentioning

confidence: 99%

Long range technology for internet of things: review, challenges, and future directions

A. Al-Shareeda,

Abdullah Alsadhan,

H. Qasim

et al. 2023

Bulletin EEI

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“…Pyramid [28] a sliding window to translate the time offsets of collided chirps to frequency and power feature for chirp decoding. PCube [29] designs a phase-based parallel decoder that can scale the concurrent transmissions of LoRa nodes with reception diversities of multiple receiving antennas of a gateway. CIC [30] adopts a spectral intersection operation to demodulate symbols via canceling out all interfering symbols.…”

Section: Related Workmentioning

confidence: 99%

AlignTrack: Push the SNR Limit of LoRa Collision Decoding

Chen

Wang

2023

IEEE/ACM Trans. Networking

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LoRa has been shown as a promising Low-Power Wide Area Network (LPWAN) technology to connect millions of devices for the Internet of Things by providing long-distance low-power communication when the SNR is very low. Real LoRa networks, however, suffer from severe packet collisions. Existing collision resolution approaches introduce a high SNR loss, i.e., require a much higher SNR than LoRa. To push the limit of LoRa collision decoding, we present AlignTrack, the first LoRa collision decoding approach that can work in the SNR limit of the original LoRa. Our key finding is that a LoRa chirp aligned with a decoding window should lead to the highest peak in the frequency domain and thus has the least SNR loss. By aligning a moving window with different packets, we separate packets by identifying the aligned chirp in each window. We theoretically prove this leads to the minimal SNR loss. In practical implementation, we address two key challenges: (1) accurately detecting the start of each packet, and (2) separating collided packets in each window in the presence of CFO and inter-packet interference. We implement AlignTrack on HackRF One and compare its performance with the state-of-the-arts. The evaluation results show that AlignTrack improves network throughput by 1.68× compared with NScale and 3× compared with CoLoRa.

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“…Several works have addressed the application scenarios of LLNs [6,7]. Among the works making contributions to LoRa, some propose alternative implementations [8] and improvements [9,10] to the physical layer. Other works step up in the protocol stack and propose improvements to the LoRaWAN architecture [11,12].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks

Coutinho,

Afonso,

Lopes

2023

Future Internet

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LoRa is one of the most popular low-power wireless network technologies for implementation of the Internet of Things, with the advantage of providing long-range communication, but lower data rates, when compared with technologies such as Zigbee or Bluetooth. LoRa is a single-channel physical layer technology on top of which LoRaWAN implements a more complex multi-channel network with enhanced functionalities, such as adaptive data rate. However, LoRaWAN relies on expensive hardware to support these functionalities. This paper proposes a LoRa data-link-layer architecture based on a multi-layer star network topology that adapts relevant LoRa parameters for each end node dynamically taking into account its link distance and quality in order to balance communication range and energy consumption. The developed solution is comprised of multiple components, including a LoRa parameter calculator to help the user to configure the network parameters, a contention-free MAC protocol to avoid collisions, and an adaptive spreading factor and transmission power mechanism. These components work together to ensure a more efficient use of the chosen ISM band and end node resources, but with low-cost implementation and operation requirements.

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PCube: Scaling LoRa Concurrent Transmissions with Reception Diversities

Cited by 11 publications

References 63 publications

Long range technology for internet of things: review, challenges, and future directions

Long range technology for internet of things: review, challenges, and future directions

AlignTrack: Push the SNR Limit of LoRa Collision Decoding

An Efficient Adaptive Data-Link-Layer Architecture for LoRa Networks

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