论文标题
在深度学习方法中预测5G下行链路调度的频道质量指标
Predicting Channel Quality Indicators for 5G Downlink Scheduling in a Deep Learning Approach
论文作者
论文摘要
5G网络提供了更多的带宽和更复杂的控制，以增强用户的体验，同时还需要更准确地估算与以前的移动网络相比的通信渠道。在本文中，我们在深度学习方法中提出了一种通道质量指标（CQI）预测方法，即长期记忆（LSTM）算法。在5G新广播（NR）系统中引入了一个在线培训模块，以减少过时的CQI对通信退化的负面影响，尤其是在高速移动性场景中。首先，我们分析了过时的CQI在5G NR系统的下行计划中的影响。然后，我们设计了一个数据生成和在线培训模块，以评估NS-3中的预测方法。仿真结果表明，所提出的LSTM方法优于进发神经网络（FNN）方法在改善下行链路传输的系统性能方面。我们的研究可能会提供有关设计新的深度学习算法以增强5G NR系统网络性能的见解。
5G networks provide more bandwidth and more complex control to enhance user's experiences, while also requiring a more accurate estimation of the communication channels compared with previous mobile networks. In this paper, we propose a channel quality indicator (CQI) prediction method in a deep learning approach in that a Long Short-Term Memory (LSTM) algorithm. An online training module is introduced for the downlink scheduling in the 5G New Radio (NR) system, to reduce the negative impact of outdated CQI for communication degradation, especially in high-speed mobility scenarios. First, we analyze the impact of outdated CQI in the downlink scheduling of the 5G NR system. Then, we design a data generation and online training module to evaluate our prediction method in ns-3. The simulation results show that the proposed LSTM method outperforms the Feedforward Neural Networks (FNN) method on improving the system performance of the downlink transmission. Our study may provide insights into designing new deep learning algorithms to enhance the network performance of the 5G NR system.
