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Effects of channel SNR in mobile cognitive radios and coexisting deployment of cognitive wireless sensor networks

机译:信道SNR在移动认知无线电中的影响和认知无线传感器网络的共存部署。

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In this paper, we describe the cognitive radios sharing the spectrum with licensed users and its effects on operational coexistence with unlicensed users. Due to the unlicensed spectrum band growing needs and usage by many IEEE 802.11 protocols, normal wireless radio operation sees high interference leading to high error rates on operational environments. We study the licensed bands and the characteristics of the unlicensed bands in general and more specific to radio characterization of individual radios and cognitive deployment of sensor networks and its effect on lifetime. The cognitive radio signals detection algorithm for this probabilistic model for the unlicensed users, uses a mobility model which takes into account the threshold variable ratio Eb/No and also calculates the lower-bound of the combined value of secondary user interference for overlapping frequencies with the primary user. By using simulation, we detect the primary user when the radio frequencies are known a priori and compare it when the frequencies are unknown. In our analysis we exploit the similarity measure seen at each sub-channel frequency, which are due to multiple paths of the same reflected signal by maximizing the correlated information of the correlation matrix. For the general case the co-variance matrix for blind source separation, we use ICA de-correlation methods and show that cognitive radio can efficiently identify users in complex situations. The effects of large deployment and cognitive sensor network are studied for a family of 802.15.4 radios adapting to power-aware algorithms.
机译:在本文中,我们描述了与许可用户共享频谱的认知无线电及其对非许可用户操作共存的影响。由于无执照频谱带的增长需求和许多IEEE 802.11协议的使用,正常的无线操作会遇到高干扰,从而导致操作环境中的高错误率。我们研究一般情况下的许可频段和非许可频段的特性,并且更具体地研究单个无线电的无线电特性和传感器网络的认知部署及其对寿命的影响。针对这种针对非许可用户的概率模型的认知无线电信号检测算法,使用了一种移动性模型,该模型考虑了阈值变量比率E b / N o 并计算了频率与主要用户重叠的次要用户干扰组合值的下限。通过使用模拟,我们可以先验先验已知射频的主要用户,并在未知时比较它。在我们的分析中,我们通过最大化相关矩阵的相关信息,来利用在每个子信道频率上看到的相似性度量,这些相似性度量是由于同一反射信号的多条路径引起的。对于一般情况下用于盲源分离的协方差矩阵,我们使用ICA解相关方法,并表明认知无线电可以有效地识别复杂情况下的用户。针对适用于功率感知算法的802.15.4无线电家族,研究了大型部署和认知传感器网络的影响。

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