Novel backscatter communication techniques enable battery-free sensor tags to interoperate with unmodified standard IoT devices, extending a sensor network's capabilities in a scalable manner. Without requiring additional dedicated infrastructure, the battery-free tags harvest energy from the environment, while the IoT devices provide them with the unmodulated carrier they need to communicate. A schedule coordinates the provision of carriers for the communications of battery-free devices with IoT nodes. Optimal carrier scheduling is an NP-hard problem that limits the scalability of network deployments. Thus, existing solutions waste energy and other valuable resources by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning scheduler that leverages graph neural networks to efficiently provide near-optimal carrier scheduling. We train our scheduler with relatively small optimal schedules obtained from a constraint optimization solver, achieving a performance within 3% of the optimal scheduler. Without the need to retrain, DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x larger in the number of tags than those used for training, breaking the scalability limitations of the optimal scheduler and reducing carrier utilization by up to 50% compared to the state-of-the-art heuristic. Our scheduler efficiently reduces energy and spectrum utilization in backscatter networks.

翻译：新型反向散射通信技术使得无电池传感器标签能够与未经修改的标准物联网设备互操作，以可扩展的方式增强传感器网络的能力。无需额外专用基础设施，无电池标签从环境中收集能量，而物联网设备为其提供通信所需未调制载波。调度机制协调了无电池设备与物联网节点通信的载波供给。最优载波调度是NP难问题，限制了网络部署的可扩展性。因此，现有方案通过次优调度浪费了能耗和其他宝贵资源。我们提出DeepGANTT，这是一种利用图神经网络高效提供近最优载波调度的深度学习调度器。我们使用约束优化求解器生成的相对较小的最优调度集训练调度器，性能达到最优调度器的3%以内。无需重新训练，DeepGANTT可泛化至节点数6倍、标签数10倍于训练规模的网络，突破了最优调度器的可扩展性限制，相比现有启发式算法将载波利用率降低高达50%。我们的调度器高效减少了反向散射网络中的能量与频谱利用。