Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion
Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion
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DOI：
10.1109/tcsi.2017.2729779
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发表时间：
2018-02-01
影响因子：
5.1
通讯作者：
Studer, Christoph
中科院分区：
文献类型：
--
作者：
Pelissier, Michael;Studer, Christoph
从宽带射频（RF）信号中提取特征，如频谱占用、干扰能量和类型或到达方向，在越来越多的应用中得到应用，因为它增强了具有认知能力的射频收发器，并使传统射频链的参数调谐成为可能。在功率和成本有限的应用中，例如，对于物联网中的传感器节点，使用传统的奈奎斯特速率模数转换器进行宽带RF特征提取是不可实现的。然而，许多射频特征的结构（如信号稀疏性）使压缩感知（CS）技术能够以亚奈奎斯特速率获取此类信号；虽然这种基于cs的模拟-信息（A2I）转换器具有实现低成本和节能的宽带射频传感的潜力，但它们受到各种现实世界的限制，例如噪声折叠、低灵敏度、混叠和有限的灵活性。本文提出了一种新的基于cs的A2I结构，称为非均匀小波采样。我们的解决方案直接在射频域中提取精心选择的小波系数子集，从而减轻了现有A2I转换器架构的主要问题。对于多波段射频信号，我们提出了一种特殊的变体，称为非均匀小波带通采样（NUWBS），通过利用多波段信号结构进一步提高灵敏度并降低硬件复杂性。我们使用仿真来证明NUWBS接近基于l(1)范数的稀疏信号恢复的理论性能极限。我们研究了硬件设计方面，并展示了小波生成阶段的ASIC测量结果，这突出了NUWBS在成本和功耗有限的应用中广泛的射频特征提取任务的有效性。
Feature extraction, such as spectral occupancy, interferer energy and type, or direction-of-arrival, from wideband radio-frequency (RF) signals finds use in a growing number of applications as it enhances RF transceivers with cognitive abilities and enables parameter tuning of traditional RF chains. In power and cost limited applications, e.g., for sensor nodes in the Internet of Things, wideband RF feature extraction with conventional, Nyquist-rate analog-to-digital converters is infeasible. However, the structure of many RF features (such as signal sparsity) enables the use of compressive sensing (CS) techniques that acquire such signals at sub-Nyquist rates; while such CS-based analog-to-information (A2I) converters have the potential to enable low-cost and energy-efficient wideband RF sensing, they suffer from a variety of real-world limitations, such as noise folding, low sensitivity, aliasing, and limited flexibility. This paper proposes a novel CS-based A2I architecture called non-uniform wavelet sampling. Our solution extracts a carefully-selected subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of existing A2I converter architectures. For multi-band RF signals, we propose a specialized variant called non-uniform wavelet bandpass sampling (NUWBS), which further improves sensitivity and reduces hardware complexity by leveraging the multi-band signal structure. We use simulations to demonstrate that NUWBS approaches the theoretical performance limits of l(1)-norm-based sparse signal recovery. We investigate hardware-design aspects and show ASIC measurement results for the wavelet generation stage, which highlight the efficacy of NUWBS for a broad range of RF feature extraction tasks in cost-and power-limited applications.