摘要
压缩采样(CS,又称压缩感知)技术的出现为频谱感知在更宽的频谱范围探测稀疏信号带来了革命性的契机。这里将观测矩阵优化与压缩采样自适应过程相结合,提出了一种优化的自适应压缩宽带频谱感知算法。此外,还引入交叉验证理论,从而保证了信号采样阶段的自动终止,以防止浪费硬件资源。理论分析和实验仿真表明,相比于传统自适应压缩感知方法,所提算法能够在较低信号采样率的情况下获得满意的信号恢复精度。
The emergence of compressive sampling （CS, also known as compressive sensing） technology brings a revolutionary opportunity to spectrum sensing for its detecting the sparse signals in broader spec- trum span. In combination of measurement matrix optimization and adaptive process of compressive sam- pling a modified adaptive compressive wideband spectrum sensing algorithm is proposed. In addition, a CV （Cross Validation） method is introduced, thus to guarantee the automatic termination of signal acquisition stage, and avoid the waste of hardware resources. Theoretical analysis and experimental simulations indi- cate that, compared with traditional CS recovery algorithm, the proposed algorithm could achieve satisfac- tory signal recovery precision in the situation of fairly low sampling rate.
出处
《通信技术》
2016年第1期62-67,共6页
Communications Technology
基金
国家自然科学基金青年项目(No.61301160)~~
关键词
认知无线电
宽带频谱感知
自适应压缩采样
观测矩阵优化
cognitive radio, wideband spectrum sensing, adaptive compressive sampling, measurement matrix optimization
