CurveALOHA: Non-linear Chirps Enabled High Throughput Random Channel Access for LoRa
CurveALOHA: Non-linear Chirps Enabled High Throughput Random Channel Access for LoRa
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DOI：
10.1109/infocom48880.2022.9796757
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Chenning Li;Zhichao Cao;Li Xiao
中科院分区：
文献类型：
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作者：
Chenning Li;Zhichao Cao;Li Xiao
远程广域网（LoRaWAN）利用线性调频脉冲进行数据调制，以其低功耗和长距离通信而闻名，能够以低成本连接海量物联网设备。然而，由于与默认随机信道访问（即 ALOHA）频繁发生冲突，LoRaWAN 吞吐量远远落后于密集和大规模物联网部署的需求。最近，一些工作实现了有效的 LoRa 载波侦听以避免碰撞。然而，持续的退避使得网络吞吐量很容易饱和，并降低LoRa端节点的能源效率。在本文中，我们提出了 CurveALOHA，一种全新的媒体访问控制方案，通过采用非线性调频脉冲的准正交逻辑信道来提高随机信道访问的吞吐量。首先，我们凭经验证明非线性线性调频可以实现与线性线性调频类似的噪声容限能力。然后，我们观察到多个非线性线性调频可以创建新的逻辑通道，这些逻辑通道与线性通道以及彼此之间准正交。最后，给定一组非线性线性调频脉冲，我们设计了两种随机线性调频脉冲选择方法，以保证端节点能够访问冲突概率较小的信道。我们使用软件定义无线电实施 CurveALOHA，并在室内和室外环境中进行了大量实验。结果表明，CurveALOHA 的网络吞吐量比最先进的载波侦听 MAC 高出 59.6%。
Long Range Wide Area Network (LoRaWAN), using the linear chirp for data modulation, is known for its low-power and long-distance communication to connect massive Internet-of-Things devices at a low cost. However, LoRaWAN throughput is far behind the demand for the dense and large-scale IoT deployments, due to the frequent collisions with the by-default random channel access (i.e., ALOHA). Recently, some works enable an effective LoRa carrier-sense for collision avoidance. However, the continuous back-off makes the network throughput easily saturated and degrades the energy efficiency at LoRa end nodes. In this paper, we propose CurveALOHA, a brand-new media access control scheme to enhance the throughput of random channel access by embracing non-linear chirps enabled quasi-orthogonal logical channels. First, we empirically show that non-linear chirps can achieve similar noise tolerance ability as the linear one does. Then, we observe that multiple nonlinear chirps can create new logical channels which are quasi-orthogonal with the linear one and each other. Finally, given a set of non-linear chirps, we design two random chirp selection methods to guarantee an end node can access a channel with less collision probability. We implement CurveALOHA with the software-defined radios and conduct extensive experiments in both indoor and outdoor environments. The results show that CurveALOHA’s network throughput is 59.6% higher than the state-of-the-art carrier-sense MAC.