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雷达卡
摘要翻译：
提出了一种基于同步码频调制(CFIM)的相对简单的低复杂度多用户通信系统。该结构在不降低数据速率的前提下，降低了发射端的发射能量以及基于正交频分复用(OFDM)方案的峰均功率比(PAPR)。在我们介绍的方案中，我们实现了一种联合码频指数调制(CFIM)，以提高频谱和能量效率。在介绍和分析后一个指标的结构后，我们推导了瑞利衰落信道下误码率性能的闭式表达式，并通过仿真验证了结果。仿真结果表明，在误码率方面，本文提出的CFIM算法明显优于现有的基于索引调制(IM)的系统，如空间调制(SM)、OFDM-IM和OFDM方案。为了更好地展示所提方案的特殊性，对PAPR、复杂度、频谱效率(SE)和能量效率(EE)进行了深入的研究。结果表明，与上述系统相比，在确保提高可靠性的同时，具有较高的SE。此外，该概念被扩展到同步多用户通信网络，在该网络中获得了完整的功能。该系统具有低复杂度、低功耗和高数据速率的特点，将成为物联网（IoT）应用的杰出代表。
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英文标题：
《A Joint Code-Frequency Index Modulation for Low-complexity, High
Spectral and Energy Efficiency Communications》
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作者：
Minh Au, Georges Kaddoum, Francois Gagnon, and Ebrahim Soujeri
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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：
A relatively simple low complexity multiuser communication system based on simultaneous code and frequency index modulation (CFIM) is proposed in this paper. The proposed architecture reduces the emitted energy at the transmitter as well as the peak-to-average-power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM)- based schemes functions without relegating data rate. In the scheme we introduce here, we implement a joint code- frequency- index modulation (CFIM) in order to enhance spectral and energy efficiencies. After introducing and analysing the structure with respect to latter metrics, we derive closed-form expressions of the bit error rate (BER) performance over Rayleigh fading channels and we validate the outcome by simulation results. Simulation are used verify the analyses and show that, in terms of BER, the proposed CFIM outperforms the existing index modulation (IM) based systems such as spatial modulation (SM), OFDM-IM and OFDM schemes significantly. To better exhibit the particularities of the proposed scheme, PAPR, complexity, spectral efficiency (SE) and energy efficiency (EE) are thoroughly examined. Results indicate a high SE while ensuring an elevated reliability compared to the aforementioned systems. In addition, the concept is extended to synchronous multiuser communication networks, where full functionality is obtained. With the characteristics demonstrated in this work, the proposed system would constitute an exceptional nominee for Internet of Things (IoT) applications where low-complexity, low-power consumption and high data rate are paramount.
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PDF链接：
https://arxiv.org/pdf/1712.07951
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