Bandwidth Is Not Enough: "Hidden" Outlier Noise and Its Mitigation

A. V. Nikitin, R. Davidchack
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Abstract

In addition to ever-present thermal noise, various communication and sensor systems can contain significant amounts of interference with outlier (e.g. impulsive) characteristics. Such outlier interference (including that caused by nonlinear signal distortions, e.g. clipping) can be efficiently mitigated in real-time using intermittently nonlinear filters. Depending on the interference nature and composition, improvements in the quality of the signal of interest achieved by such filtering will vary from "no harm" to substantial. In this tutorial, we explain in detail why the underlying outlier nature of interference often remains obscured, discussing the many challenges and misconceptions associated with state-of-art analog and/or digital nonlinear mitigation techniques, especially when addressing complex practical interference scenarios. We then focus on the methodology and tools for real-time outlier noise mitigation, demonstrating how the "excess band" observation of outlier noise enables its efficient in-band mitigation. We introduce the basic real-time nonlinear components that are used for outlier noise filtering and provide examples of their implementation. We further describe complementary nonlinear filtering arrangements for wide- and narrow-band outlier noise reduction, providing several illustrations of their performance and the effect on channel capacity. Finally, we outline "effectively analog" digital implementations of these filtering structures, discuss their broader applications, and comment on the ongoing development of the platform for their demonstration and testing. To emphasize the effectiveness and versatility of this approach, in our examples we use particularly challenging waveforms that severely obscure low-amplitude outlier noise, such as broadband chirp signals (e.g. used in radar, sonar, and spread-spectrum communications) and "bursty," high crest factor signals (e.g. OFDM).
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带宽不够:“隐藏的”离群噪声及其缓解
除了始终存在的热噪声之外,各种通信和传感器系统还可能包含大量具有异常值(例如脉冲)特性的干扰。这种异常干扰(包括由非线性信号失真引起的干扰,例如剪切)可以使用间歇性非线性滤波器有效地实时减轻。根据干扰的性质和组成,通过这种滤波获得的感兴趣的信号质量的改善将从“无害”到实质性的改善。在本教程中,我们详细解释了为什么干扰的潜在异常性质经常被掩盖,讨论了与最先进的模拟和/或数字非线性缓解技术相关的许多挑战和误解,特别是在处理复杂的实际干扰场景时。然后,我们重点介绍了实时异常噪声缓解的方法和工具,展示了异常噪声的“多余波段”观察如何实现其有效的带内缓解。我们介绍了用于离群噪声滤波的基本实时非线性分量,并给出了它们的实现示例。我们进一步描述了用于宽频带和窄频带异常噪声降低的互补非线性滤波安排,提供了它们的性能和对信道容量的影响的几个示例。最后,我们概述了这些滤波结构的“有效模拟”数字实现,讨论了它们更广泛的应用,并对其演示和测试平台的持续发展进行了评论。为了强调这种方法的有效性和通用性,在我们的示例中,我们使用了特别具有挑战性的波形,这些波形严重模糊了低幅度的异常噪声,例如宽带啁啾信号(例如用于雷达,声纳和扩频通信)和“突发”,高波峰因子信号(例如OFDM)。
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