On the LoRa Modulation for IoT: Optimal Preamble Detection and Its Performance Analysis

Kang, Jae-Mo; Lim, Dong-Woo; Kang, Kicheon

doi:10.1109/jiot.2021.3108139

Cited by 10 publications

(9 citation statements)

References 28 publications

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“…1) Start Position Detection: For the received collided signal, we first detect the number of collided packets by adopting a preamble detection method [17], [18], and then calculate the accurate start position of each packet by calculating and eliminating the influence of CFO. 2) Aligned Chirp Detection: After knowing the start position of each packet, we calculate the positions of all chirps in received signal and apply a moving window to align with these chirps in chirp partition step.…”

Section: Aligntrack Design a Overviewmentioning

confidence: 99%

“…Packets Number Calculation: We find the LoRa preamble [17], [18] by finding N p (the number of baseline up-chirp in a preamble) consecutive peaks at the same frequency. However, when there is a LoRa payload consisting more than N p consecutive identical chirps, there will also be N p Fig.…”

Section: A Consecutive Identical Chirpsmentioning

confidence: 99%

“…Augustin et al [16] provides an overview of LoRa and an in-depth analysis of its functional components. Kang et al [17] thoroughly analyzes the performance of the preamble detection model in the LoRa demodulation process and develops a optimal preamble detection scheme to maximum the detection probability. FlipLoRa [50] encodes packets with interleaved upchirps and downchirps instead of only using upchirps as in LoRa.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Aligntrack Design a Overviewmentioning

confidence: 99%

Section: A Consecutive Identical Chirpsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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AlignTrack: Push the SNR Limit of LoRa Collision Decoding

Chen

Wang

2023

IEEE/ACM Trans. Networking

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“…Long Range (LoRa) based on chirp spread spectrum is a widely adopted technique developed for Internet of Things (IoT) [1]. One of the important initial tasks in operating an LoRa system is to detect the presence of preamble, also known as the preamble detection [2], [3], [4], [5], of which result (or success) critically affects subsequent major tasks, such as synchronization, channel estimation, and demodulation.…”

Section: Introductionmentioning

confidence: 99%

“…In [2], the theoretical optimal performance of LoRa preamble detection was investigated. To achieve such optimal performance, however, the hardware complexity needs to increase inevitably (such as due to matrix inversion and averaging), eventually leading to incomplete compatibility with LoRa.…”

Section: Introductionmentioning

confidence: 99%

LoRa Preamble Detection With Optimized Thresholds

Kang

2023

IEEE Internet Things J.

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Long Range (LoRa) is one of the widely adopted techniques for Internet of Things (IoT). Preamble detection is a key initial task for LoRa systems. Meanwhile, the so-called threshold-based preamble detection is a common technique for compatibility with LoRa. However, the existing methods on the threshold-based LoRa preamble detection suffer from low performance because the detection thresholds are heuristically chosen. To tackle this issue, in this letter, we aim to optimize those thresholds by maximizing the preamble detection probability while satisfying a constraint on false alarm rate. For this purpose, coherent and noncoherent procedures for the preamble detection are presented in a universal manner, followed by conducting their performance analysis. Simulation results demonstrate the superiority and effectiveness of the proposed scheme.

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