A new approach to signal classification using spectral correlation and neural networks
A new approach to signal classification using spectral correlation and neural networks
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DOI：
10.1109/dyspan.2005.1542629
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发表时间：
2005-12
期刊：
影响因子：
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通讯作者：
A. Fehske;J. Gaeddert;Jeffrey H. Reed
中科院分区：
文献类型：
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作者：
A. Fehske;J. Gaeddert;Jeffrey H. Reed
作为占用免许可频段的无线通信系统认知无线电的潜在补充，信道感测和频谱分配长期以来一直受到人们的关注。已经提出了循环频谱分析的传统方法作为对载波频率和带宽未知的应用进行信号分类的方法，但是其计算复杂并且需要大量的观察时间才能获得足够的性能。神经网络已用于信号分类，但仅适用于存在基带信号的情况。通过结合这些技术，可以开发出更高效、更可靠的分类器，其中大量处理是离线执行的，从而减少了在线计算。在本文中，我们重新审视使用谱相干性和神经网络的信号分类，其性能通过蒙特卡罗模拟来表征
Channel sensing and spectrum allocation has long been of interest as a prospective addition to cognitive radios for wireless communications systems occupying license-free bands. Conventional approaches to cyclic spectral analysis have been proposed as a method for classifying signals for applications where the carrier frequency and bandwidths are unknown, but is, however, computationally complex and requires a significant amount of observation time for adequate performance. Neural networks have been used for signal classification, but only for situations where the baseband signal is present. By combining these techniques a more efficient and reliable classifier can be developed where a significant amount of processing is performed offline, thus reducing online computation. In this paper we take a renewed look at signal classification using spectral coherence and neural networks, the performance of which is characterized by Monte Carlo simulations