论文标题

通过多包装接收的快速可靠的Lora物理层数据聚合

Quick and Reliable LoRa Physical-layer Data Aggregation through Multi-Packet Reception

论文作者

You, Lizhao, Tang, Zhirong, Wang, Pengbo, Wang, Zhaorui, Dai, Haipeng, Fu, Liqun

论文摘要

本文介绍了直接在物理层中直接在物理层中汇总数据(例如,总和,平均,最小,最大)的远距离(LORA)物理层数据聚合系统(Lorapda)。特别是,在协调一些节点以同时传输数据后,网关利用新的多包装接收方法(MPR)方法来计算从相位 - 同步叠加信号的聚合数据。与需要额外的功率同步和相同步的模拟方法不同,我们的基于MRP的数字方法与商业Lora节点兼容,并且更可靠。 Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of节点的数据和3)使用多个序列的可能性来提高解码性能的软性数据包解码算法。跟踪驱动的仿真结果表明,在物理层吞吐量方面,符号解调算法的表现使最先进的MPR解码器的表现优于5.3 $ \ times $,而软解码器对于不可避免的不良阶段失误和估计错误在实践中更强大。此外,Lorapda在网络吞吐量方面,所有SNR的最先进MPR方案的表现至少为2.1 $ \ times $,这表明了快速可靠的数据聚合。

This paper presents a Long Range (LoRa) physical-layer data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average, min, max) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the phase-asynchronous superimposed signal. Different from the analog approach which requires additional power synchronization and phase synchronization, our MRP-based digital approach is compatible with commercial LoRa nodes and is more reliable. Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes' data, and 3) a soft-decision packet decoding algorithm that utilizes the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5.3$\times$ in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms the state-of-the-art MPR scheme by at least 2.1$\times$ for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.

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