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
翻译:降低能源消耗是低功率机器型通信(MTC)网络的一个紧迫问题。在这方面,旨在将机器型设备(MTDs)无线电界面消耗的能量减少到最低程度的觉醒信号技术(WuS)是一个大有希望的解决办法。然而,最先进的WuS机制使用静态操作参数,因此无法有效地适应系统动态。为了克服这一点,我们设计了一个简单而有效的神经网络,以预测MTC交通模式并相应地配置WUS。我们提议的预测Wus(FWuS)利用了准确的短期内存(LSTM)的交通预测,通过避免经常在闲置状态中进行页面监测来延长MTDs的睡眠时间。模拟结果显示了我们的方法的有效性。交通预测误差低于4%,是虚假的警报和误测概率,分别低于8.8%和1.3%。在能源消耗减少方面,FWuS可以超越最佳基准机制,达到32%。最后,我们证明FUS-TC的动态能性能向低速度变化。