Aim: We set out to investigate the benefit of the “memory” of long short term memory (LSTM) networks in predicting spectrum occupancy in multiple time horizons in Land Mobile Radio (LMR) bands. Background: ANNs are a popular choice for spectrum prediction. Traditionally, ARIMA models have been at the forefront of forecasting. However, recurrent ANNs have demonstrated good prediction performance. Methodology: We train and evaluate four prediction models: a baseline which simply delays the time series, a seasonal ARIMA model, a TDNN and an LSTM. We test their performance on an hourly dataset in LMR bands collected in Ottawa, Canada between the dates of October 2016 and April 2017. Results: We find that LSTMs provide an improvement in prediction performance compared to the other models. We also compare the computational complexity of our models. Conclusions: The LSTM networks that remember long term dependencies and designed to work with time series provide an improvement accurately predicting spectrum occupancy in LMR bands.

中文翻译：

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陆地移动无线电频段频谱预测神经网络中记忆影响的实验结果

目标：我们着手研究长期短期记忆 (LSTM) 网络的“记忆”在预测陆地移动无线电 (LMR) 频段中多个时间范围内的频谱占用情况方面的优势。背景：人工神经网络是频谱预测的流行选择。传统上，ARIMA 模型一直处于预测的前沿。然而，循环神经网络已表现出良好的预测性能。方法：我们训练和评估四种预测模型：简单延迟时间序列的基线、季节性 ARIMA 模型、TDNN 和 LSTM。我们在 2016 年 10 月至 2017 年 4 月期间在加拿大渥太华收集的 LMR 波段的每小时数据集上测试了它们的性能。结果：我们发现 LSTM 与其他模型相比在预测性能方面有所改进。我们还比较了模型的计算复杂度。