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基于转置采样的宽带频谱感知技术

Wideband spectrum sensing technology based on transposed sampling
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摘要 随着通信技术的不断发展,信号带宽的增加和信号复杂度的提升使得频谱感知,尤其是宽带频谱感知面临精确度和硬件实现等诸多挑战。为改善宽带频谱感知中感知精度低、采样资源需求大的问题,提出了一种新的基于转置采样的宽带频谱感知架构——转置调制宽带转换器(transposed modulated wideband converter,TMWC)。为重构原始信号频谱,TMWC架构以信号矩阵边界的非0元素作为目标,基于转置采样模型,通过固定采样间隔的测量矩阵和估计支撑集恢复原始频谱。TMWC只需要一部分的信号频谱用于恢复频谱支撑,从而降低了信号矩阵稀疏度,实现了理论最小采样率采样。仿真结果表明,在低信噪比和低采样率的情况下,TMWC架构对多频带信号具有较好的感知性能。对于不同稀疏度的信号,TMWC架构具有比传统宽带频谱感知架构更强的感知性能。 With the continuous development of communication technology,the increase of signal bandwidth and signal complexity pose numerous challenges to spectrum sensing,especially wideband spectrum sensing,in terms of accuracy and hardware implementation.To address the issues of low sensing accuracy and high sampling resource demand in wideband spectrum sensing,a new wideband spectrum sensing architecture based on transposed sampling,the transposed modulated wideband converter(TMWC),was proposed.To reconstruct the original signal spectrum,the TMWC architecture targets the non-zero elements at the boundary of the signal matrix.Based on the transposed sampling model,a measurement matrix with fixed sampling interval and the estimated support set were used to recover the original spectrum.The TMWC architecture only requires a portion of the signal spectrum for the recovery of the spectral support,reducing the sparsity of the signal matrix and achieving the theoretical minimum sampling rate.Simulation results show that the TMWC architecture has a good sensing performance for multi-band signals at low SNR and low sampling rate.For signals with different sparsities,the TMWC architecture exhibits stronger sensing performance than the traditional wideband spectrum sensing architecture.
作者 肖东 张正敏 侯燕曦 王健 XIAO Dong;ZHANG Zhengmin;HOU Yanxi;WANG Jian(School of Data Science,Fudan University,Shanghai 200433,China)
出处 《信息对抗技术》 2024年第6期60-70,共11页 Information Countermeasure Technology
基金 国家自然科学基金资助项目(61971146)。
关键词 宽带频谱感知 压缩感知 欠奈奎斯特采样 稀疏重构算法 wideband spectrum sensing compressed sensing sub-Nyquist sampling sparse reconstruction algorithm
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