In a dynamic spectrum allocation (DSA) system, reliable prediction of spectrum occupancy based on a spectrum consumption model (SCM) is critical for system design, performance analysis, and evaluation. In this article, we focus on a low-level abstracted measured dataset from a massive campaign and investigate the occupancy of representative frequency bands. First, we apply an autoregressive-moving-average (ARMA) model combined with a low-pass filter, given the stationarity of the channel measurement dataset and thanks to the computational simplicity of the model. The average received power and off-state probability are extracted from the measured data. According to the results, the measured and predicted data are in good agreement. Comparing the proposed model-based ARMA with the popular long short-term memory learning algorithm, they have similar error accuracy with pre-processed data, while ARMA has a much lower training complexity. In the second step, we develop an SCM describing the spectrum usage for designing and examining the DSA system. We extract the periodic, aperiodic low-frequency, and burst components of the time series. Also, a binary sequence is extracted from a sparse occupancy channel, and modelled by a non-homogeneous Markov chain. Results show that the model-generated data can maintain the same statistics as the measured data.

中文翻译：

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动态频谱接入系统的频谱占用预测和建模

在动态频谱分配 (DSA) 系统中，基于频谱消耗模型 (SCM) 的可靠频谱占用预测对于系统设计、性能分析和评估至关重要。在本文中，我们关注来自大规模活动的低级抽象测量数据集，并调查代表性频段的占用情况。首先，考虑到信道测量数据集的平稳性以及模型的计算简单性，我们将自回归移动平均 (ARMA) 模型与低通滤波器相结合。从测量数据中提取平均接收功率和断态概率。结果表明，实测数据与预测数据吻合良好。将提出的基于模型的 ARMA 与流行的长短期记忆学习算法进行比较，它们与预处理数据具有相似的错误精度，而 ARMA 的训练复杂度要低得多。在第二步中，我们开发了一个描述频谱使用的 SCM，用于设计和检查 DSA 系统。我们提取时间序列的周期性、非周期性低频和突发分量。此外，从稀疏占用信道中提取二进制序列，并通过非齐次马尔可夫链建模。结果表明，模型生成的数据可以保持与测量数据相同的统计数据。从稀疏占用信道中提取二进制序列，并通过非齐次马尔可夫链建模。结果表明，模型生成的数据可以保持与测量数据相同的统计数据。从稀疏占用信道中提取二进制序列，并通过非齐次马尔可夫链建模。结果表明，模型生成的数据可以保持与测量数据相同的统计数据。