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无线Ad hoc网络传输容量的性能分析

ISSN号：1009-5896
期刊名称：电子与信息学报
时间：2012
页码：1457-1462
分类：TP183[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程] TP18[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
作者机构：[1]Key Laboratory on Wideband Wireless Communications and Sensor Network Technology,Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
相关基金：supported by the Program for National Natural Science Foundation of China （61171094, 61071092）, the National Science ＆ Technology Key Project （2011ZX03001-006-02, 2011ZX03005-004-03）, and the Key Project of Jiangsu Provincial Natural Science Foundation （BK2011027）
相关项目：基于随机几何的多跳无线网络干扰模型及跨层容量优化技术研究

关键词： BP神经网络, 压缩, 感知, NYQUIST, NP-HARD, 信号重建, 频谱, 检测过程, spectrum sensing, compressed sensing, BP neural network

中文摘要：

这份报纸建议压缩察觉到(CS ) 策划重建并且估计信号。在这个计划， CS 的框架被用来打破 Nyquist 采样限制，使重建并且比传统地被要求的估计经由更少大小的信号可能。然而，重建算法基于 CS 是通常非确定的多项式难(NP 难) 在数学，它在获得即时分析结果造成困难。因此，一个新压缩察觉到的计划基于背繁殖(BP ) ，神经网络在一个假设下面被建议每个亚乐队是一样。在这个新计划， BP 神经网络被增加进察觉过程，为信号重建和决策代替。由做这，在重建的重计算费用被移动进预先训练时期，能在即时分析前被做，引起及时消费的锋利的减小。为简化， 1 位的 quantification 在压缩信号上被拿。模拟在建议计划表明表演改进：与正常基于 CS 的计划相比，建议的那经由更少大小介绍一短得多的反应时间以及更好的坚韧性表演给噪音。

英文摘要：

This paper proposes a compressed sensing （CS） scheme to reconstruct and estimate the signals. In this scheme, the framework of CS is used to break the Nyquist sampling limit, making it possible to reconstruct and estimate signals via fewer measurements than that is required traditionally. However, the reconstruction algorithms based on CS are normally non-deterministic polynomial hard （NP-hard） in mathematics, which makes difficulties in obtaining real-time analysis-results. Therefore, a new compressed sensing scheme based on back propagation （BP） neural network is proposed under an assumption that every sub-band is the same. In this new scheme, BP neural network is added into detection process, replacing for signal reconstruction and decision-making. By doing this, heavy calculation cost in reconstruction is moved into pre-training period, which can be done before the real-time analysis, bringing about a sharp reduction in time consuming. For simplify, 1-bit quantification is taken on compressed signals. Simulations demonstrate the performance enhancement in the proposed scheme： compared with normal CS-based scheme, the proposed one presents a much shorter response time as well as a better robustness performance to noise via fewer measurements.

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