广义频分复用链路质量模型

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摘要翻译：
5G系统旨在实现极高的数据速率、低端到端时延和超低功耗。近年来，人们对5G物理层波形的设计产生了相当大的兴趣。一个重要的候选者是广义频分复用(GFDM)。为了评估其性能和特点，应在一系列方案中进行系统级研究。然而，如果使用比特级模拟器进行这些研究，则需要高度复杂的计算。本文将常用于正交频分复用(OFDM)系统级研究的基于互信息(MI)的链路质量模型（PHY抽象）应用于GFDM。利用该模型对GFDM波形进行了性能测试，并对不同信道类型的GFDM波形进行了比特级仿真。此外，本文还对基于GFDM的LTE-A系统在实际场景中的系统级研究进行了比较，并分别使用比特级仿真器和该抽象模型进行了比较。结果表明，利用实际信道数据，该模型具有较高的精度。基于这些结果，PHY抽象技术可以有效地、低复杂度地评估基于GFDM的系统性能。与比特级仿真相比，抽象情况下的分组错误率(PER)和吞吐量结果的最大差异不超过4%，同时提供了大约62,000倍的仿真节省时间。
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英文标题：
《A Link Quality Model for Generalised Frequency Division Multiplexing》
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作者：
Ghaith R. Al-Juboori, David Halls, Angela Doufexi and Andrew R. Nix
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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：
  5G systems aim to achieve extremely high data rates, low end-to-end latency and ultra-low power consumption. Recently, there has been considerable interest in the design of 5G physical layer waveforms. One important candidate is Generalised Frequency Division Multiplexing (GFDM). In order to evaluate its performance and features, system-level studies should be undertaken in a range of scenarios. These studies, however, require highly complex computations if they are performed using bit-level simulators. In this paper, the Mutual Information (MI) based link quality model (PHY abstraction), which has been regularly used to implement system-level studies for Orthogonal Frequency Division Multiplexing (OFDM), is applied to GFDM. The performance of the GFDM waveform using this model and the bit-level simulation performance is measured using different channel types. Moreover, a system-level study for a GFDM based LTE-A system in a realistic scenario, using both a bit-level simulator and this abstraction model, has been studied and compared. The results reveal the accuracy of this model using realistic channel data. Based on these results, the PHY abstraction technique can be applied to evaluate the performance of GFDM based systems in an effective manner with low complexity. The maximum difference in the Packet Error Rate (PER) and throughput results in the abstraction case compared to bit-level simulation does not exceed 4% whilst offering a simulation time saving reduction of around 62,000 times.
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PDF链接：
https://arxiv.org/pdf/1710.09495

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