Compressed Tensor Completion: Approach for UAV-Aided 3-D Radio Map Construction

Abstract

In urban area, 2-D radio map is no longer sufficient for spectrum management, highlighting the growing significance of 3-D radio map. However, traditional methods for constructing 3-D radio map face challenges, including high storage requirements and computational complexity, limiting their effectiveness. To address these issues, this article introduces a systematic approach for constructing 3-D radio maps. Initially, a portion of the real-world 3-D city map is captured and discretized into grids of uniform size. Subsequently, spectrum data is collected using an unmanned-aerial-vehicle (UAV) platform. To enhance sampling efficiency, iterative path planning algorithms refine sampling locations to capture the spectrum conditions of the target space. Finally, the trajectory tensor of the entire spectrum space is constructed through adaptive embedding, and the global spectrum is compressed and reconstructed by using the enhanced tensor singular value decomposition (t-SVD) algorithm to construct the radio map of the city scenario. Comparing our algorithm with the existing methods, we analyze the influence factors of frequency-spatial compression sampling on the spectrum situation and strength recovery. Numerical results show that, compared with the existing methods, the proposed method achieves more accurate spectrum mapping.