BICM-OFDM中帧错误概率预测的深度学习方法 系统

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摘要翻译：
在无线通信的背景下，我们提出了一种深度学习方法来学习从频率选择性衰落信道的瞬时状态到任意传输参数下相应的帧错误概率(FEP)的映射。提出了比特交织编码调制(BICM)正交频分复用(OFDM)链路链的抽象模型，并证明了模型参数的最大似然(ML)估计器估计了真实的FEP分布。此外，我们利用深度神经网络作为一种通用工具来实现我们的模型，并提出了一种训练方案，该方案即使在使用二进制帧错误事件（即ACKs/NACKs）进行训练时，网络输出也收敛到与输入信道状态有关的FEP。仿真结果表明，在一定的信道码率范围内，与传统的有效指数SIR度量(EESM)方法相比，我们的方法在FEP预测精度上有所提高，并表明这些提高可以用来提高链路吞吐量。
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英文标题：
《Deep Learning for Frame Error Probability Prediction in BICM-OFDM
  Systems》
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作者：
Vidit Saxena, Joakim Jald\'en, Mats Bengtsson, and Hugo Tullberg
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最新提交年份：
2017
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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：
  In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution. Further, we exploit deep neural networks as a general purpose tool to implement our model and propose a training scheme for which, even while training with the binary frame error events (i.e., ACKs / NACKs), the network outputs converge to the FEP conditioned on the input channel state. We provide simulation results that demonstrate gains in the FEP prediction accuracy with our approach as compared to the traditional effective exponential SIR metric (EESM) approach for a range of channel code rates, and show that these gains can be exploited to increase the link throughput.
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PDF链接：
https://arxiv.org/pdf/1710.1127

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关键词：学习方法 深度学习 BIC ICM Applications state 信道 提出 方法 参数