A homotopy estimation based temporal-spatial spectrum prediction for UAV communications with arbitrary flight paths.

Due to the rapid growth of unmanned aerial vehicles (UAVs), their spectrum resources become scarce, leading to UAVs requiring spectrum prediction to share the spectrum with other users. However, contemporary prediction methods may have difficulty in predicting the spectrum states at the next location, because the UAVs cannot obtain the historical data in advance to train prediction models. This paper introduces a temporal-spatial spectrum prediction approach for arbitrary flight within a specific region. The main issue involves the estimation of historical data at the next location during flight, accomplished through the concept of homotopy theory (HT). First, the HT is extended from two objects to multiple objects. Then, the historical data is estimated by homotopy mapping, which is derived by the boundary conditions of the HT and the physical meanings of the model parameters. Finally, the spectrum is predicted by the hidden Markov model (HMM) using the HT estimated data, referring to the multiple objects HT-HMM (MOHT-HMM) based prediction method. The main innovation is to use the HT to estimate the historical data at the next location, avoiding the non-stationarity and correlation issues of the spectra. Experimental results using real measured civil aviation data show the efficacy of the MOHT-HMM in accurately predicting UAV spectrum during arbitrary flights within a preset area.