摘要翻译：
软件无线电(SDR)已成为通信性能测试和实现的常用工具。SDR方法的优点包括：可重新配置的设计、对变化条件的适应性响应、高效的开发和高度通用的实现。为了了解软件无线电的好处，美国宇航局格伦研究中心(GRC)提出了空间电信无线电系统(STRS)和标准应用程序接口(API)结构。系统的每个组件都使用定义良好的API与其他组件通信。标准API的好处是为添加选项放宽了每个组件的平台限制。例如，波形产生过程可以支持现场可编程门阵列(FPGA)、个人计算机(PC)或嵌入式系统。只要API定义了需求，生成的波形选择将与完整的系统一起工作。本文介绍了基于STRS和标准API协议的自适应软件无线电的设计和开发。详细介绍了软件无线电实验台系统，包括控制图形用户界面(GUI)、数据库、GNU无线电硬件控制和通用软件无线电外设(USRP)前端。此外，还对空间通信SDR方法的有效性进行了性能评估。
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英文标题：
《An adaptive software defined radio design based on a standard space
  telecommunication radio system API》
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作者：
Wenhao Xiong, Xin Tian, Genshe Chen, Khanh Pham, Erik Blasch
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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：
  Software defined radio (SDR) has become a popular tool for the implementation and testing for communications performance. The advantage of the SDR approach includes: a re-configurable design, adaptive response to changing conditions, efficient development, and highly versatile implementation. In order to understand the benefits of SDR, the space telecommunication radio system (STRS) was proposed by NASA Glenn research center (GRC) along with the standard application program interface (API) structure. Each component of the system uses a well-defined API to communicate with other components. The benefit of standard API is to relax the platform limitation of each component for addition options. For example, the waveform generating process can support a field programmable gate array (FPGA), personal computer (PC), or an embedded system. As long as the API defines the requirements, the generated waveform selection will work with the complete system. In this paper, we demonstrate the design and development of adaptive SDR following the STRS and standard API protocol. We introduce step by step the SDR testbed system including the controlling graphic user interface (GUI), database, GNU radio hardware control, and universal software radio peripheral (USRP) tranceiving front end. In addition, a performance evaluation in shown on the effectiveness of the SDR approach for space telecommunication.
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PDF链接：
https://arxiv.org/pdf/1711.09311