AlignTrack:突破LoRa碰撞解码的极限

Qian Chen, Jiliang Wang
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引用次数: 22

摘要

LoRa已被证明是一种很有前途的低功耗广域网(LPWAN)技术,通过在非常低的信噪比下提供长距离低功耗通信,可以连接数百万台物联网设备。然而,真正的LoRa网络存在严重的数据包冲突。现有的碰撞分辨率方法引入了高信噪比损失,即需要比LoRa高得多的信噪比。为了突破LoRa碰撞解码的限制,我们提出了AlignTrack,这是第一个可以在原始LoRa的信噪比限制下工作的LoRa碰撞解码方法。我们的关键发现是,与解码窗口对齐的LoRa啁啾应该导致频域中的峰值,因此具有最小的信噪比损失。通过将移动窗口与不同的数据包对齐,我们通过识别每个窗口中对齐的啁啾来分离数据包。我们从理论上证明了这会导致最小的信噪比损失。在实际实现中,我们解决了两个关键挑战:(1)准确检测每个数据包的开始,(2)在存在CFO和包间干扰的情况下,在每个窗口中分离碰撞数据包。我们在HackRF One上实现了AlignTrack,并将其性能与最先进的性能进行了比较。评估结果表明,AlignTrack比NScale提高了1.68倍的网络吞吐量,比CoLoRa提高了3倍。
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AlignTrack: Push the Limit of LoRa Collision Decoding
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 in a very low SNR. 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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