A Blind Spatial Spectrum Sensing Algorithm for Noncircular Signals

In cognitive radio, most spatial spectrum sensing algorithms ignore the characteristic of the noncircular (NC) signals that are common in modern communication systems. In this letter, we propose a novel blind spatial spectrum sensing algorithm for NC signals. First, the spatial spectrum of the NC signals is improved by utilizing the complementary covariance. On this basis, we present the maximum-minimum spatial spectrum of the NC signals (NC-MMSS) detection, which can detect the signal of primary user (PU) and provide corresponding angle of arrival (AoA) information. Then the theoretical expressions of probability of false alarm, decision threshold, and probability of detection are derived. Finally, simulation results verify that the proposed method is superior to the existing spatial spectrum sensing algorithms.

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Publication Details

Journal
IEEE Communications Letters
Published
2025-02-07
DOI
https://doi.org/10.1109/lcomm.2025.3539762
Primary Topic
Blind Source Separation Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

A Blind Spatial Spectrum Sensing Algorithm for Noncircular Signals

Yuan Jiang, Lei Zhao, Xianjian Qiu
IEEE Communications Letters
Blind Source Separation Techniques
article

A Blind Spatial Spectrum Sensing Algorithm for Noncircular Signals

Yuan Jiang, Lei Zhao, Xianjian Qiu
article en

Abstract

In cognitive radio, most spatial spectrum sensing algorithms ignore the characteristic of the noncircular (NC) signals that are common in modern communication systems. In this letter, we propose a novel blind spatial spectrum sensing algorithm for NC signals. First, the spatial spectrum of the NC signals is improved by utilizing the complementary covariance. On this basis, we present the maximum-minimum spatial spectrum of the NC signals (NC-MMSS) detection, which can detect the signal of primary user (PU) and provide corresponding angle of arrival (AoA) information. Then the theoretical expressions of probability of false alarm, decision threshold, and probability of detection are derived. Finally, simulation results verify that the proposed method is superior to the existing spatial spectrum sensing algorithms.

IEEE Communications LettersVol. 29(4)
Sun Yat-sen University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Openalex Percentile: Top 98%
Blind Source Separation Techniques
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A Blind Spatial Spectrum Sensing Algorithm for Noncircular Signals — Yuan Jiang, Lei Zhao, et al. · IEEE Communications Letters (2025) | TGRS Research Map | TGRS