A Method of Space-frequency Compressed Sensing on Wideband Spectrum Detection
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摘要: 压缩感知(CS)技术使快速宽频检测成为可能，采用CS技术的WSN可为认知无线电用户提供频谱信息。针对WSN检测到的频谱数据，该文建立了一种基于空域和频域2维压缩的空频压缩感知(SFCS)模型，提出了相应的重构算法，并详细分析了SFCS的各种性能。仿真结果证实了在同一检测概率下，SFCS模型下比传统模型所需传输数据更少；在相同总压缩率下，该文算法下的ROC性能优于传统算法的性能。Abstract: Compressed Sensing (CS) technology paves the way for quick wideband spectrum detection and the WSN based on CS can provide Cognitive Radio (CR) users with the spectrum information. For the spectrum data detected in WSN, a two-dimensional compression Space-Frequency Compressed Sensing (SFCS) model is established in the space and frequency domain, and the corresponding algorithm for reconstruction is proposed and the performance of SFCS is analyzed. The simulation results show that SFCS needs less transmitted data than the traditional model on the same detection probability and the performance of Receiver Operating Characteristic (ROC) in the algorithm is better than that of traditional method on the same total compressed rate.
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