摘要翻译：
频谱感知在动态频谱共享中起着至关重要的作用，是解决无线频谱短缺的一种有前途的技术。尤其是正交频分复用(OFDM)信号的检测，作为一种被广泛接受的多载波传输模式，受到了人们的极大关注。尽管做出了各种努力，但大多数传统的OFDM感知方法都存在噪声不确定性、定时延迟和载波频率偏移(CFO)等问题，这些问题显著降低了感知精度。为了解决这些问题，本文在深度学习网络的支持下开发了两个新的OFDM感知框架。具体来说，我们首先提出了一种基于堆叠式自动编码器的频谱感知方法(SAE-SS)，其中设计了一个堆叠式自动编码器网络来提取OFDM信号的固有特征。利用这些特征对OFDM用户的活动进行分类，与传统的OFDM感知方法相比，SAE-SS对噪声不确定性、定时延迟和CFO具有更强的鲁棒性。此外，SAE-SS不需要传统的基于特征的OFDM感知方法所必需的任何信号先验信息（如信号结构、导频、循环前缀）。为了进一步提高SAE-SS的感知精度，特别是在低信噪比条件下，我们提出了一种基于时频域信号的叠加自动编码器的频谱感知方法（SAE-TF）。SAE-TF以较高的计算复杂度为代价，获得了比SAW-SS更高的传感精度。大量的仿真结果表明，SAE-SS和SAE-TF都能获得比现有方法更高的感知精度，而现有方法存在噪声不确定性、定时延迟和CFO等问题。
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英文标题：
《Deep Learning Network Based Spectrum Sensing Methods for OFDM Systems》
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作者：
Qingqing Cheng and Zhenguo Shi and Diep N. Nguyen and Eryk Dutkiewicz
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最新提交年份：
2019
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising technology to address the radio spectrum shortage. In particular, sensing of Orthogonal frequency division multiplexing (OFDM) signals, a widely accepted multi-carrier transmission paradigm, has received paramount interest. Despite various efforts, most conventional OFDM sensing methods suffer from noise uncertainty, timing delay and carrier frequency offset (CFO) that significantly degrade the sensing accuracy. To address these challenges, this work develops two novel OFDM sensing frameworks drawing support from deep learning networks. Specifically, we first propose a stacked autoencoder based spectrum sensing method (SAE-SS), in which a stacked autoencoder network is designed to extract the inherent features of OFDM signals. Using these features to classify the OFDM user's activities, SAE-SS is much more robust to noise uncertainty, timing delay, and CFO than the conventional OFDM sensing methods. Moreover, SAE-SS doesn't require any prior information of signals (e.g., signal structure, pilot tones, cyclic prefix) which are essential for the conventional feature-based OFDM sensing methods. To further improve the sensing accuracy of SAE-SS, especially under low SNR conditions, we propose a stacked autoencoder based spectrum sensing method using time-frequency domain signals (SAE-TF). SAE-TF achieves higher sensing accuracy than SAW-SS at the cost of higher computational complexity. Extensive simulation results show that both SAE-SS and SAE-TF can achieve significantly higher sensing accuracy, compared with state of the art approaches that suffer from noise uncertainty, timing delay and CFO.
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PDF链接：
https://arxiv.org/pdf/1807.09414