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Adaptive blind wideband spectrum sensing for cognitive radio based on sub-Nyquist sampling technique

机译：基于亚奈奎斯特采样技术的认知无线电自适应盲宽带频谱感知

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摘要

Spectrum sensing is the core process in cognitive radio networks. Spectrum sensing of wideband signal results in enhancement of system reliability and effectiveness. In this paper, a modified system of wideband spectrum sensing based on non-uniform Multi-coset samplers is proposed. The proposed system is designed to work on totally blind input wideband signal. Lomb-Scargle periodogram is used to detect the number of active bands (N). System parameters are selected to decrease the average sub-Nyquist sampling frequency that results in activating lower number of parallel branches of non-uniform samplers. Then, a lower power consumption and complexity is achieved. The proposed system is able to adapt its operating parameters, time offsets and number of active branches continuously. Simulation results show accurate reconstructed signal, high probability of detection and low errors even at a small number of sub-Nyquist samples and low SNR.
机译：频谱感测是认知无线电网络的核心过程。宽带信号的频谱感测可以增强系统的可靠性和有效性。本文提出了一种基于非均匀多陪集采样器的宽带频谱感知改进系统。拟议的系统设计用于在全盲输入宽带信号上工作。 Lomb-Scargle周期图用于检测活动频段的数量（N）。选择系统参数以降低平均子奈奎斯特采样频率，这会导致激活非均匀采样器的并行分支数量减少。然后，实现了较低的功耗和复杂性。所提出的系统能够连续地调整其操作参数，时间偏移和活动分支的数量。仿真结果表明，即使在少量的子奈奎斯特采样和低SNR的情况下，重构信号也准确，检测概率高，错误率低。

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