LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-of-Things (IoT) devices. The dense deployment and an increasing number of IoT devices lead to intense collisions due to uncoordinated transmissions. However, the current MAC/PHY design of LoRaWAN fails to recover collisions, resulting in degraded performance as the system scales. This article presents FTrack, a novel communication paradigm that enables demodulation of collided LoRa transmissions. FTrack resolves LoRa collisions at the physical layer and thereby supports parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features. The proposed technique is motivated from two key observations: (1) the symbol edges of the same frame exhibit periodic patterns, while the symbol edges of different frames are usually misaligned in time; (2) the frequency of LoRa signal increases continuously in between the edges of symbol, yet exhibits sudden changes at the symbol edges. We detect the continuity of signal frequency to remove interference and further exploit the time-domain information of symbol edges to recover symbols of all collided frames. We substantially optimize computation-intensive tasks and meet the real-time requirements of parallel LoRa decoding. We implement FTrack on a low-cost software defined radio. Our testbed evaluations show that FTrack demodulates collided LoRa frames with low symbol error rates in diverse SNR conditions. It increases the throughput of LoRaWAN in real usage scenarios by up to 3 times.

中文翻译：

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FTrack：LoRa传输的并行解码

LoRa已经成为一种有前途的低功耗广域网（LP-WAN）技术，可以连接大量的物联网（IoT）设备。由于传输不协调，密集的部署和越来越多的IoT设备导致严重的冲突。但是，LoRaWAN的当前MAC / PHY设计无法恢复冲突，导致系统扩展时性能下降。本文介绍了FTrack，这是一种新颖的通信范例，能够解调冲突的LoRa传输。FTrack解决了物理层的LoRa冲突，从而支持LoRa传输的并行解码。我们提出了一种通过共同考虑时域和频域特征来分离冲突传输的新颖技术。所提出的技术基于两个关键的观察结果：（1）同一帧的符号边缘表现出周期性的模式，而不同帧的符号边缘通常在时间上未对齐；（2）LoRa信号的频率在符号边缘之间连续增加，但在符号边缘处呈现突然变化。我们检测信号频率的连续性以消除干扰，并进一步利用符号边缘的时域信息来恢复所有冲突帧的符号。我们充分优化了计算密集型任务，并满足了并行LoRa解码的实时性要求。我们在低成本的软件无线电中实施FTrack。我们的测试平台评估表明，FTrack在各种SNR条件下以低符号错误率对冲突的LoRa帧进行解调。在实际使用情况下，它可以将LoRaWAN的吞吐量提高多达3倍。