Using power measurements collected by a network of low-cost sensors, power spectral density (PSD) maps are constructed to capture the distribution of RF power across space and frequency. Linearly compressed and quantized power measurements enable wideband sensing at affordable implementation complexity using a small number of bits. Strengths of data- and model-driven approaches are combined to develop estimators capable of incorporating multiple forms of spectral and propagation prior information while fitting the rapid variations of shadow fading across space. To this end, novel nonparametric and semiparametric formulations are investigated. It is shown that the desired PSD maps can be obtained using support vector machine-type solvers. In addition to batch approaches, an online algorithm attuned to real-time operation is developed. Numerical tests assess the performance of the novel algorithms.

利用一组低成本传感器收集的功率测量值构建功率谱密度图，通过引入线性压缩和量化通信测量并结合数据驱动和模型驱动方法以估计多种光谱和传播先前信息，展示了利用支持向量机类型求解器可获得功率谱密度图，并开发了一种在线算法用于实时操作，数值测试评估了新算法的性能。

从量化功率测量中学习功率谱图