Joint Allocation Strategies of Power and Spreading Factors With Imperfect Orthogonality in LoRa Networks
Joint Allocation Strategies of Power and Spreading Factors With Imperfect Orthogonality in LoRa Networks
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DOI：
10.1109/tcomm.2020.2974722
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发表时间：
2020-06-01
影响因子：
8.3
通讯作者：
Guitton, Alexandre
中科院分区：
文献类型：
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作者：
Amichi, Licia;Kaneko, Megumi;Guitton, Alexandre
LoRa物理层是未来物联网（IoT）应用中最有前途的低功耗广域网（LPWAN）技术之一。它通过向终端设备分配不同的扩频因子（SF）和发射功率来提供覆盖范围和数据速率的灵活适配。我们专注于提高吞吐量的公平性，同时降低能耗。鉴于大多数现有的方法假设完美的SF正交性，并忽略SF间干扰的有害影响，我们制定了一个联合SF和功率分配问题，以最大限度地提高终端设备的最小上行链路吞吐量，共同SF和SF间干扰和功率约束。这导致混合整数非线性优化，为了易于处理，其被分成两个子问题：首先，用于固定发射功率的SF分配，以及其次，给定先前获得的分配解决方案的功率分配。对于第一个子问题，我们提出了一个低复杂度的SF和终端设备之间的多对一匹配算法。对于第二个问题，考虑到它的复杂性，我们使用两种类型的约束近似：线性化和二次版本。我们的性能评估表明，建议的SF分配和功率优化方法，使大幅提高各种性能目标，如吞吐量，公平性和功耗，他们优于基线计划。
The LoRa physical layer is one of the most promising Low Power Wide-Area Network (LPWAN) technologies for future Internet of Things (IoT) applications. It provides a flexible adaptation of coverage and data rate by allocating different Spreading Factors (SFs) and transmit powers to end-devices. We focus on improving throughput fairness while reducing energy consumption. Whereas most existing methods assume perfect SF orthogonality and ignore the harmful effects of inter-SF interferences, we formulate a joint SF and power allocation problem to maximize the minimum uplink throughput of end-devices, subject to co-SF and inter-SF interferences and power constraints. This results into a mixed-integer non-linear optimization, which, for tractability, is split into two sub-problems: firstly, the SF assignment for fixed transmit powers, and secondly, the power allocation given the previously obtained assignment solution. For the first sub-problem, we propose a low-complexity many-to-one matching algorithm between SFs and end-devices. For the second one, given its intractability, we transform it using two types of constraints' approximation: a linearized and a quadratic version. Our performance evaluation demonstrates that the proposed SF allocation and power optimization methods enable to drastically enhance various performance objectives such as throughput, fairness and power consumption, and that they outperform baseline schemes.