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雷达卡
摘要翻译：
多维(MD)映射为比特交织迭代译码编码调制（BICM-ID）的映射设计提供了更大的灵活性，并潜在地提高了带宽效率。然而，对于高阶信号星座，由于可能存在大量的MD映射，寻找合适的MD映射是一项非常复杂的任务。本文提出了一种新的映射方法来构造有效的MD映射，以改善BICM-ID在瑞利衰落信道上的误码性能。我们提出将MD映射设计问题分解为四个不同的$2$-D映射函数。$2$-D映射被设计成使得所得到的MD映射在低信噪比(SNRs)下改善BICM-ID误码性能。我们还开发了代价函数，这些代价函数可以被优化以改善高信噪比下的误码性能。与已有的映射方法相比，该映射方法非常简单，并且可以得到适合于不同调制的MD映射，包括BICM-ID的高阶调制。仿真结果表明，在目标误码率为$10^{-6}$时，我们的映射明显优于已知的映射。我们的映射还提供了一个较低的错误层相比，他们的众所周知的同行。
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英文标题：
《Novel Method for Multi-Dimensional Mapping of Higher Order Modulations
for BICM-ID Over Rayleigh Fading Channels》
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作者：
Hassan M. Navazi and Md. Jahangir Hossain
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最新提交年份：
2018
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Information Theory 信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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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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一级分类：Mathematics 数学
二级分类：Information Theory 信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
Multi-dimensional (MD) mapping offers more flexibility in mapping design for bit-interleaved coded modulation with iterative decoding (BICM-ID) and potentially improves the bandwidth efficiency. However, for higher order signal constellations, finding suitable MD mappings is a very complicated task due to the large number of possible mappings. In this paper, a novel mapping method is introduced to construct efficient MD mappings to improve the error performance of BICM-ID over Rayleigh fading channels. We propose to break the MD mapping design problem into four distinct $2$-D mapping functions. The $2$-D mappings are designed such that the resulting MD mapping improves the BICM-ID error performance at low signal to noise ratios (SNRs). We also develop cost functions that can be optimized to improve the error performance at high SNRs. The proposed mapping method is very simple compared to well-known mapping methods, and it can achieve suitable MD mappings for different modulations including higher order modulations for BICM-ID. Simulation results show that our mappings significantly outperform the previously known mappings at a target bit error rate (BER) of $10^{-6}$. Our mappings also offer a lower error-floor compared to their well-known counterparts.
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PDF链接：
https://arxiv.org/pdf/1807.04854
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