Combining Software-Defined Radio Learning Modules and Neural Networks for Teaching Communication Systems Courses

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Luis A. Camuñas-Mesa, José M. de la Rosa
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引用次数: 0

Abstract

The paradigm known as Cognitive Radio (CR) proposes a continuous sensing of the electromagnetic spectrum in order to dynamically modify transmission parameters, making intelligent use of the environment by taking advantage of different techniques such as Neural Networks. This paradigm is becoming especially relevant due to the congestion in the spectrum produced by increasing numbers of IoT (Internet of Things) devices. Nowadays, many different Software-Defined Radio (SDR) platforms provide tools to implement CR systems in a teaching laboratory environment. Within the framework of a ‘Communication Systems’ course, this paper presents a methodology for learning the fundamentals of radio transmitters and receivers in combination with Convolutional Neural Networks (CNNs).
结合软件无线电学习模块和神经网络进行通信系统课程教学
认知无线电(Cognitive Radio, CR)提出了一种对电磁频谱的连续感知,以便动态修改传输参数,通过利用神经网络等不同技术,智能地利用环境。由于越来越多的IoT(物联网)设备产生的频谱拥塞,这种模式变得尤为重要。目前,许多不同的软件定义无线电(SDR)平台提供了在教学实验室环境中实现CR系统的工具。在“通信系统”课程的框架内,本文提出了一种结合卷积神经网络(cnn)学习无线电发射机和接收机基础知识的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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