论文标题
多台化认知物联网网络中EH短包通信的有效的深入CNN设计
An Efficient Deep CNN Design for EH Short-Packet Communications in Multihop Cognitive IoT Networks
论文作者
论文摘要
在本文中，我们设计了一个有效的深卷卷神经网络（CNN），以改善和预测多跳认知互联网（IoT）网络中能量收集（EH）短包通信的性能。具体而言，我们提出了一个sum-eh方案，该方案允许物联网节点从功率信标或初级发射器收集能量，以不仅提高数据包传输，还可以提高能量收集能力。然后，我们基于提出的SUM-EH方案来构建一个具有功能增强收集块的新型Deep CNN框架，以同时估算块错误率（BLER）并以高精度和较低的执行时间估算吞吐量。仿真结果表明，所提出的CNN框架几乎完全实现了sum-eh One的笨拙和吞吐量，同时大大降低了计算复杂性，这表明在复杂方案下，IoT系统的实时设置。此外，设计的CNN模型在所考虑的数据集上实现了$ {1.33 \ times10^{ - 2}} $ $ {1.33 \ times10^{ - 2}} $的根平方 - eRROR（RMSE），该数据集与深层神经网络和先进的机器学习方法相比表现出最低的RMSE。
In this paper, we design an efficient deep convolutional neural network (CNN) to improve and predict the performance of energy harvesting (EH) short-packet communications in multi-hop cognitive Internet-of-Things (IoT) networks. Specifically, we propose a Sum-EH scheme that allows IoT nodes to harvest energy from either a power beacon or primary transmitters to improve not only packet transmissions but also energy harvesting capabilities. We then build a novel deep CNN framework with feature enhancement-collection blocks based on the proposed Sum-EH scheme to simultaneously estimate the block error rate (BLER) and throughput with high accuracy and low execution time. Simulation results show that the proposed CNN framework achieves almost exactly the BLER and throughput of Sum-EH one, while it considerably reduces computational complexity, suggesting a real-time setting for IoT systems under complex scenarios. Moreover, the designed CNN model achieves the root-mean-square-error (RMSE) of ${1.33\times10^{-2}}$ on the considered dataset, which exhibits the lowest RMSE compared to the deep neural network and state-of-the-art machine learning approaches.
