使用认知无线电应用的LSTM模型进行频谱占用预测
Tamizhelakkiya Kolangiyappan1, Sabitha Gauni1,2, Prabhu Chandhar3
1Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.
概括
本研究介绍了一种长短期记忆 (LSTM) 模型,用于认知无线电网络中的频谱占用预测. 该LSTM方法提高了移动流量预测的准确性,以实现高效的频谱管理.
科学领域:
- 电信工程 电信工程 电信工程
- 无线网络的机器学习
背景情况:
- 移动流量预测对于下一代蜂网络的频谱管理至关重要,特别是在认知无线电 (CR) 应用中.
- 准确的预测可以实现高效的动态频谱访问和资源分配.
研究的目的:
- 为基于基础设施的蜂交通系统提出和评估基于长期短期记忆 (LSTM) 的频谱占用预测 (SOP) 方法.
- 将各种LSTM模型的性能与传统的统计方法进行比较.
主要方法:
- 通过监测九个长期演化 (LTE) 频道的频谱活动,创建了一个二进制数据集.
- 多个LSTM模型 (卷积,CNN,堆叠,双向) 被线下训练并在数据集上测试.
- 用平均绝对误差 (MAE) 评估预测性能.
主要成果:
- 与自动回归集成移动平均线 (ARIMA) 模型相比,提出的基于LSTM的SOP模型显示出更高的预测准确性.
- 该LSTM模型实现了2.5%更高的预测准确度,有效地将交通趋势与实际样本结合起来.
结论:
- 基于LSTM的模型为蜂网络的频谱占用预测提供了显著的改进.
- 这种方法有助于在CR应用中更有效地管理频谱和利用资源.
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