用可微分高阶马尔可夫模型提升无线频谱预测精度
Differentiable High-Order Markov Models for Spectrum Prediction
- 通过可微分概率转移矩阵融合传统马尔可夫与深度学习优势
- 在小样本含异常值场景下性能优于主流深度学习方法
- 适用于频谱资源紧张的动态无线环境，适合通信系统优化
深度学习和循环神经网络的兴起革新了时间序列处理领域，近期频谱预测研究多聚焦于此。然而，频谱预测作为较早发展的领域，在2010年代已有大量经典方法，如马尔可夫模型。本文重新审视动态无线环境中高阶马尔可夫模型在频谱预测中的应用，提出一个框架以解决感知长度与模型阶数不匹配、状态空间复杂度随阶数增大等问题。进一步地，通过梯度驱动的监督学习实现概率转移矩阵的微调，构建了概率建模与现代机器学习融合的混合方法。基于真实Wi-Fi流量的仿真表明，该高阶马尔可夫模型在数据受限且含异常值的场景中表现优异，竞争力超过多种深度学习方法。
原文摘要 · Abstract (English)
The advent of deep learning and recurrent neural networks revolutionized the field of time-series processing. Therefore, recent research on spectrum prediction has focused on the use of these tools. However, spectrum prediction, which involves forecasting wireless spectrum availability, is an older field where many "classical" tools were considered around the 2010s, such as Markov models. This work revisits high-order Markov models for spectrum prediction in dynamic wireless environments. We introduce a framework to address mismatches between sensing length and model order as well as state-space complexity arising with large order. Furthermore, we extend this Markov framework by enabling fine-tuning of the probability transition matrix through gradient-based supervised learning, offering a hybrid approach that bridges probabilistic modeling and modern machine learning. Simulations on real-world Wi-Fi traffic demonstrate the competitive performance of high-order Markov models compared to deep learning methods, particularly in scenarios with constrained datasets containing outliers.
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