Radio Environment Map Construction Using Super-Resolution Imaging for Intelligent Transportation Systems

Radio environment map (REM) has emerged as a crucial technology to improve the robustness of intelligent transportation systems (ITS) by enhancing network planning and spectrum resource utilization. To construct a precise REM, optimizing deployment of sensor nodes and increasing spatial interpolation accuracy are two main directions. Given the deployment of sensor nodes, high resolution (HR) spatial interpolation would still bring about huge computing overhead, which is not practical for realtime applications. In order to improve the efficiency and accuracy of REM construction, we propose a super-resolution (SR) based REM construction method, which is composed of Kriging interpolation, dictionary learning and random forest. In our method, both low resolution (LR) and HR REM image sets are generated and trained to obtain a random forest model. With spectrum data from the limited number of sensor nodes, a SR REM can be acquired by the proposed method. Simulation results demonstrate that our method can greatly shorten the construction time of REM while maintaining high accuracy.