论文标题
基于流量感知的长期记忆预测的机器型设备的节能唤醒信号
Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction
论文作者
论文摘要
减少能源消耗是低功率机型通信（MTC）网络中的一个紧迫问题。在这方面，旨在最大程度地减少机器型设备（MTDS）无线电接口所消耗的能量的唤醒信号（WUS）技术是一个有前途的解决方案。但是，最新的WUS机制使用静态操作参数，因此它们无法有效地适应系统动力学。为了克服这一点，我们设计了一个简单但有效的神经网络，以预测MTC流量模式并相应地配置WU。我们提出的预测WUS（FWUS）利用了基于精确的长期记忆（LSTM） - 基于流量预测，该预测允许在闲置状态下避免频繁的页面监视场合来延长MTD的睡眠时间。仿真结果显示了我们方法的有效性。流量预测错误显示为4％，分别为错误警报和未检测概率低于8.8％和1.3％。在降低能耗的方面，FWUS可以胜过最佳的基准机制，最高可达32％。最后，我们证明了FWUS动态适应流量密度变化的能力，促进了低功率MTC可伸缩性
Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the energy consumed by the radio interface of the machine-type devices (MTDs), stands as a promising solution. However, state-of-the-art WuS mechanisms use static operational parameters, so they cannot efficiently adapt to the system dynamics. To overcome this, we design a simple but efficient neural network to predict MTC traffic patterns and configure WuS accordingly. Our proposed forecasting WuS (FWuS) leverages an accurate long-short term memory (LSTM)- based traffic prediction that allows extending the sleep time of MTDs by avoiding frequent page monitoring occasions in idle state. Simulation results show the effectiveness of our approach. The traffic prediction errors are shown to be below 4%, being false alarm and miss-detection probabilities respectively below 8.8% and 1.3%. In terms of energy consumption reduction, FWuS can outperform the best benchmark mechanism in up to 32%. Finally, we certify the ability of FWuS to dynamically adapt to traffic density changes, promoting low-power MTC scalability
