RAScatter: Achieving Energy-Efficient Backscatter Readers via AI-Assisted Power Adaptation
RAScatter: Achieving Energy-Efficient Backscatter Readers via AI-Assisted Power Adaptation
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DOI：
10.1109/iotdi54339.2022.00016
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发表时间：
2022-05
期刊：
影响因子：
--
通讯作者：
Kai Huang;Ruirong Chen;Wei Gao
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文献类型：
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作者：
Kai Huang;Ruirong Chen;Wei Gao
后向散射通信降低了无电池设备的功耗，代价是从后向散射读取器传输额外的射频能量。这种额外的成本导致读取器的能量效率极低，但被总是使用最高发射RF功率以获得最大产出的现有系统所忽略。相反，我们设想，在许多实际场景中，最大吞吐量是不必要的，允许发射射频功率适应所需的吞吐量。在本文中，我们提出了一种新的背向散射系统RAScatter，它具有精确、自适应和轻量级的功率自适应功能，适用于高能效的背向散射阅读器。RAScatter通过设计模块化神经网络来学习后向散射信道条件、发射射频功率和吞吐量之间的纠缠关系，将复杂的学习任务分解为多个相互关联但又简化的子任务。这种分解避免了神经网络中的冗余，并消除了由于低速后向散射系统中训练数据不足而导致的训练中的任何混乱。在商品无电池标签上的实验结果表明，RAScatter将后向散射阅读器的能效提高了3.5倍，并将阅读器在后向散射通信中的功耗降低了80%。
Backscatter communication reduces the batteryless device's power consumption at the cost of extra RF energy transmitted from backscatter readers. Such extra cost results in extremely low energy efficiency at readers, but is ignored by existing systems that always use the highest transmit RF power for maximum goodput. Instead, we envision that the maximum goodput is unnecessary in many practical scenarios, allowing adaptation of transmit RF power to the required goodput. In this paper, we present RAScatter, a new backscatter system of precise, adaptive and lightweight power adaptation towards energy-efficient backscatter readers. RAScatter learns the entangled correlation between backscatter channel conditions, transmit RF power and goodput by designing a modular neural network, which decomposes the complex learning task into multiple related but simplified subtasks. This decomposition avoids redundancy in neural networks and eliminates any confusion in training due to insufficient training data in low-speed backscatter systems. Experiment results over commodity batteryless tags show that RAScatter improves the energy efficiency at backscatter readers by 3.5× and reduces the readers' power consumption in backscatter communication by up to 80%.