A Temporal-Spatial Spectrum Prediction Using the Concept of Homotopy Theory for UAV Communications

As unmanned aerial vehicles (UAVs) have been widely employed, the spectrum resource for UAV communication becomes scarce. The UAVs may communicate based on spectrum sharing, requiring efficient spectrum prediction to improve the performance of spectrum sensing. However, since the existing prediction methods are generally designed to predict the spectrum states in the next moment at the current location, they can not be directly applied to UAVs which rapidly move to the next location when obtaining the prediction result. In another word, the UAVs require to predict the spectrum in the next moment at the next location. This is a method which can predict spectrum in the joint temporal and spatial dimensions. The main challenge is the historical data of the next location is usually not possible to be obtained in advance by the UAVs. This paper proposes a new prediction approach which firstly estimates the historical data of the next location based on the homotopy theory (HT), and then performs the prediction based on the hidden Markov model (HMM), referring to the HT-HMM based prediction. Experimental results show that the HT-HMM based approach is able to efficiently predict the spectrum states of the next location.