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Kernelized-Likelihood Ratio Tests for Binary Phase-Shift Keying Signal Detection
IEEE Transactions on Cognitive Communications and Networking ( IF 7.4 ) Pub Date : 2020-10-09 , DOI: 10.1109/tccn.2020.3028768
Ahmadreza Salehi , Amir Zaimbashi , Mikko Valkama

In this article, kernelized-likelihood ratio tests (LRTs) for binary phase-shift keying (BPSK) signal detection based on the polynomial kernel function are proposed. Specifically, we kernelize the conventional LRT of BPSK signal detection using the so-called kernel trick, such that the inner product of the conventional LRT is replaced with proper polynomial kernel functions allowing for richer feature space to be deployed in the detection. We also derive computationally efficient recursive implementation structures for the proposed methods, resulting overall in six new detectors. With respect to the noise variance uncertainty (NVU), the proposed detectors can be divided into two general classes, namely i) constant false alarm rate (CFAR) and ii) semi-CFAR (S-CFAR) methods. To facilitate efficient operation under NVU, we also propose a new threshold-setting strategy to adjust the level of the proposed S-CFAR detectors. Additionally, we address the well-known energy detector (ED) under NVU and devise a new fixed-level ED formulation while also obtaining closed-form expressions for its false alarm and detection probabilities. Our extensive simulation results show that the proposed S-CFAR detectors outperform the state-of-the-art BPSK signal detectors with 2.4 dB signal-to-noise ratio (SNR) gain under practical worst-case NVU assumptions, while the performance gain is approximately 5.7 dB without NVU. In the case of the proposed CFAR detectors, the corresponding improvement in detection performance is approximately 1.8 dB.

中文翻译:

二进制相移键控信号检测的核似然比测试

在本文中,提出了基于多项式核函数的二进制相移键控 (BPSK) 信号检测的核似然比测试 (LRT)。具体来说,我们使用所谓的核技巧对 BPSK 信号检测的传统 LRT 进行核化,从而将传统 LRT 的内积替换为适当的多项式核函数,从而允许在检测中部署更丰富的特征空间。我们还为所提出的方法推导了计算效率高的递归实现结构,从而总共产生了六个新的检测器。关于噪声方差不确定性(NVU),所提出的检测器可以分为两大类,即i)恒定误报率(CFAR)和ii)半CFAR(S-CFAR)方法。为促进 NVU 下的高效运作,我们还提出了一种新的阈值设置策略来调整所提出的 S-CFAR 检测器的级别。此外,我们解决了 NVU 下著名的能量检测器 (ED),并设计了一种新的固定级别 ED 公式,同时还获得了其误报和检测概率的闭式表达式。我们广泛的仿真结果表明,在实际最坏情况 NVU 假设下,所提出的 S-CFAR 检测器优于最先进的 BPSK 信号检测器,信噪比 (SNR) 增益为 2.4 dB,而性能增益为约 5.7 dB,无 NVU。在建议的 CFAR 检测器的情况下,检测性能的相应改进约为 1.8 dB。我们解决了 NVU 下著名的能量检测器 (ED),并设计了一种新的固定级别 ED 公式,同时还获得了其误报和检测概率的封闭形式表达式。我们广泛的仿真结果表明,在实际最坏情况 NVU 假设下,所提出的 S-CFAR 检测器优于最先进的 BPSK 信号检测器,信噪比 (SNR) 增益为 2.4 dB,而性能增益为约 5.7 dB,无 NVU。在建议的 CFAR 检测器的情况下,检测性能的相应改进约为 1.8 dB。我们解决了 NVU 下著名的能量检测器 (ED),并设计了一种新的固定级别 ED 公式,同时还获得了其误报和检测概率的封闭形式表达式。我们广泛的仿真结果表明,在实际最坏情况 NVU 假设下,所提出的 S-CFAR 检测器优于最先进的 BPSK 信号检测器,信噪比 (SNR) 增益为 2.4 dB,而性能增益为约 5.7 dB,无 NVU。在建议的 CFAR 检测器的情况下,检测性能的相应改进约为 1.8 dB。我们广泛的仿真结果表明,在实际最坏情况 NVU 假设下,所提出的 S-CFAR 检测器优于最先进的 BPSK 信号检测器,信噪比 (SNR) 增益为 2.4 dB,而性能增益为约 5.7 dB,无 NVU。在建议的 CFAR 检测器的情况下,检测性能的相应改进约为 1.8 dB。我们广泛的仿真结果表明,在实际最坏情况 NVU 假设下,所提出的 S-CFAR 检测器优于最先进的 BPSK 信号检测器,信噪比 (SNR) 增益为 2.4 dB,而性能增益为约 5.7 dB,无 NVU。在建议的 CFAR 检测器的情况下,检测性能的相应改进约为 1.8 dB。
更新日期:2020-10-09
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