Robust Online Prediction of Spectrum Map With Incomplete and Corrupted Observations

Spectrum map is an essential tool for a range of emerging applications of 5G and 6G networks. Despite the great efforts that have been put on the construction of spectrum maps, access to accurate and valid spectrum data in dynamically changing environments emphasizes the need for more advanced solutions tailored to such rapidly varying scenarios. To this end, the idea of spectrum map prediction is introduced. In this paper, we address the problem of spectrum map prediction from historical spectrum observations in the dynamically changing environments. The problem is particularly challenging when the available historical spectrum observations are incomplete and corrupted by anomalies. We propose three techniques to solve the problem. First, we combine the spectrum map with prediction functionalities so as to offer a huge potential for efficient resource management and flexible sharing of resources in dynamically changing environments. Second, by fully exploiting the hidden spatial-temporal-spectral structures of the spectrum data and the sparsity of anomalies and missing data, we model the spectrum map as a 3rd-order spectrum tensor and formulate the spectrum map prediction problem as a low-rank tensor completion problem. Third, we design a robust online spectrum map prediction (ROSMP) algorithm based on the alternating direction minimization method, which derives the tensor decomposition factors for a new timeslot based on the update of existing ones rather than re-computing from the scratch. By gradually learning the hidden spatial-temporal-spectral structures of the spectrum data, ROSMP is able to predict and obtain the complete spectrum map with high accuracy. Finally, extensive numerical evaluations using a real spectrum measurement dataset confirm the efficacy and efficiency of ROSMP and show the superiority of ROSMP over the baselines.