Abstract
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.
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