Multi channel spectrum prediction algorithm based on GCN and LSTM
Han Zhang, Qiao Tian, Yu Han
Abstract
With the increasingly serious shortage of spectrum resources, spectrum dynamic access based on spectrum prediction technology is widely recognized. Due to the high burstiness and complex intrinsic correlation of spectrum monitoring data, high-precision multi-channel spectrum prediction is challenging. This paper constructs spectrum monitoring data as a kind of graph structure data based on the correlation of spectrum itself, and designs a graph network model combining Graph convolution network(GCN) and Long-short term memory network(LSTM) for multi-channel spectrum prediction. This paper creatively introduces the method of graph network. And GCN is used instead of CNN to extract the correlation of channels, so as to improve the accuracy of multi-channel prediction. Experiments are conducted based on a real-world spectrum measurement dataset. The results show that the model proposed in this paper has better predictive performance compared with other methods.