Radio spectrum is a limited and increasingly scarce resource, which motivates alternative usage methods such as dynamic spectrum allocation (DSA). However, DSA requires an accurate prediction of spectrum usage in both time and spatial domains with minimal sensing cost. In this paper, we propose NN-ResNet prediction model to address this challenge in two steps. First, in order to make the best use of the sensors in the region, we deploy a deep learning prediction model based on convolutional neural networks (CNNs) and residual networks (ResNets), to predict spatio-temporal spectrum usage of the region. Second, to reduce sensing cost, the nearest neighbor (NN) interpolation is applied to recover spectrum usage data in the unsensed areas. In this case, fewer sensors are needed for prediction with the help of the reconstruction procedure. The model is verified through groups of comparison simulations in terms of the sensors’ sparsity and the number of transmitters involved. In addition, the proposed model is compared with CNN and ConvLSTM prediction model. The results show that the proposed NN-ResNet model maintains a lower error rate under various sparse sensor circumstances.

中文翻译：

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使用卷积神经网络和 ResNet 的时空谱负载预测

无线电频谱是一种有限且日益稀缺的资源，这激发了诸如动态频谱分配 (DSA) 等替代使用方法。然而，DSA 需要以最小的传感成本准确预测时域和空间域中的频谱使用情况。在本文中，我们提出了 NN-ResNet 预测模型，分两步解决这一挑战。首先，为了充分利用该区域的传感器，我们部署了一个基于卷积神经网络（CNNs）和残差网络（ResNets）的深度学习预测模型，来预测该区域的时空频谱使用情况。其次，为了降低感知成本，应用最近邻（NN）插值来恢复未感知区域中的频谱使用数据。在这种情况下，借助重建过程进行预测所需的传感器更少。该模型通过多组比较模拟验证了传感器的稀疏性和所涉及的发射器的数量。此外，将所提出的模型与 CNN 和 ConvLSTM 预测模型进行了比较。结果表明，所提出的 NN-ResNet 模型在各种稀疏传感器情况下保持较低的错误率。