首页|Low-Complexity Integrated Super-Resolution Sensing and Communication with Signal Decimation and Ambiguity Removal
Low-Complexity Integrated Super-Resolution Sensing and Communication with Signal Decimation and Ambiguity Removal
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万方数据
维普
Integrated sensing and communication (ISAC) is one of the main usage scenarios for 6G wireless networks. To most efficiently uti-lize the limited wireless resources,integrated super-resolution sensing and communication (ISSAC) has been recently proposed to signifi-cantly improve sensing performance with super-resolution algorithms for ISAC systems,such as the Multiple Signal Classification (MUSIC) al-gorithm. However,traditional super-resolution sensing algorithms suffer from prohibitive computational complexity of orthogonal-frequency division multiplexing (OFDM) systems due to the large dimensions of the signals in the subcarrier and symbol domains. To address such is-sues,we propose a novel two-stage approach to reduce the computational complexity for super-resolution range estimation significantly. The key idea of the proposed scheme is to first uniformly decimate signals in the subcarrier domain so that the computational complexity is signifi-cantly reduced without missing any target in the range domain. However,the decimation operation may result in range ambiguity due to pseudo peaks,which is addressed by the second stage where the total collocated subcarrier data are used to verify the detected peaks. Com-pared with traditional MUSIC algorithms,the proposed scheme reduces computational complexity by two orders of magnitude,while maintain-ing the range resolution and unambiguity. Simulation results verify the effectiveness of the proposed scheme.
万方数据
维普
