3D Compressed Spectrum Mapping With Sampling Locations Optimization in Spectrum-Heterogeneous Environment

Spectrum mapping has emerged as an important problem in wireless communications, which generates a spectrum map for the resource analysis and management. Given constrained transceiver volume limited energy consumption, how to effectively reconstruct situation by sampling data is pressing challenge mapping. In this paper, exploiting sparse nature of situation, we firstly attempt solve three-dimensional (3D) compressed way sensing. Then, develop quadrature right-triangular (QR) pivoting based measurement matrix optimization algorithm. By iteratively selecting new dominant locations, it promotes recovery accuracy compared random measurement. After that, propose 3D spatial subspace orthogonal matching pursuit (OMP) algorithm recover Finally, simulations are presented show comparisons terms localization, source signal strength recovery, success rate recovery. Results our proposed scheme not only reduces number, but also achieves high level accuracy.