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为了提高基于循环平稳感知方法的检测性能,减少深衰落对感知性能的影响,提出了一种基于多天线合并方案的频谱感知算法,即将循环自相关函数和循环谱密度函数进行一种改进的最大增益比合并,并通过渐进最优χ2检测法得出检验统计量。该种算法可以在感知信号和噪声幅值信息未知的情况下对信号的有无进行检测。为了验证提出方法的正确性,以BPSK信号为例对提出的算法进行仿真检验,仿真结果表明多天线感知算法的性能优于单天线感知,减少了深衰落对接收信号的影响。 相似文献
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基于循环谱能量的自适应频谱检测算法 总被引:1,自引:0,他引:1
根据信号循环平稳谱的特征,研究在低信噪比环境下的频谱检测问题,提出一种基于循环谱能量的自适应判决门限频谱检测算法。该算法融合能量检测与循环平稳特征检测的机理,以信号的循环谱能量为检测统计量,加权合并虚警率与检测率,准确估计循环谱特征值,构建了具有噪声自适应能力的频谱检测判决门限。仿真结果表明,该算法可以在低信噪比环境下有效地完成频谱检测,克服了噪声波动对频谱检测性能的影响,对不同调制主信号的感知具有稳健性。与最大—最小特征值算法和盲检测算法相比,该算法分别改善了信噪比4dB和8dB。 相似文献
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针对认知网络实际环境中常呈现出噪声高动态变化、低信噪比特征,无法快速准确进行频谱感知的问题,本文将物理学非线性领域中的随机共振理论引入到频谱感知中,提出了一种基于广义随机共振的能量检测算法.该算法引入匹配噪声,通过匹配非线性系统、噪声和信号三者的关系,从而改变能量检测统计量的分布,有效地检测信号的存在性.本文从理论上推导了最佳匹配噪声的表达式,并得到了检测性能、受噪声不确定度的影响、感知时间等方面的重要理论结论.仿真结果验证了理论推导的正确性,表明所提算法能够在信噪比为-20dB等低信噪比条件下较现有能量检测算法提高3dB以上,且具有感知速度快、受噪声不确定度影响小等特点. 相似文献
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针对无线信道环境中,信道多径衰落和噪声不确定性等低信噪比情况下主用户信号检测性能较低的问题,提出一种基于循环平稳人工神经网络(ANN)的主用户信号频谱感知算法。该算法首先对信号特征参数进行提取,作为训练样本和待测样本,再采用ANN算法分别对有无主用户情况下的信号进行分类检测。仿真实验表明,与能量检测法(ED)和循环平稳特征检测法(CD)相比较,所提算法可在低信噪比情况下,不受噪声不确定性等因素影响,具有较高的分类检测性能,有效地实现了对主用户信号的感知。 相似文献
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为了发现空间中的“频谱空洞”而加以利用以使频谱利用率最大化,频谱感知技术得到了广泛关注。已有基于特征矢量的频谱感知算法因涉及大量特征值分解运算导致算法运算量大,不适应实时检测。本文提出的频谱感知算法利用信号子空间和噪声子空间之间的正交性,将次用户接收信号分别投影到上述子空间,根据投影值的差异实现快速频谱感知。理论分析和仿真结果表明本文提出的算法与已有算法相比有效降低了运算量,检测性能不受噪声不确定度影响、不需要预知主用户先验知识和噪声方差,且低信噪比、小采样情况下有更优越的检测性能。 相似文献
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本文提出了一种新的对周期平稳信号进行检测以及对二阶周期循环频率进行估计的算法。该算法利用信号的递归性质构造高阶自相关矩阵,并通过利用周期平稳信号与自相关矩阵特征值和特征向量的关系,对其进行检测以及对循环频率进行估计。传统检测周期平稳信号的算法是通过计算其循环自相关函数或循环谱实现,相比传统算法而言,本算法由于利用到了信号更多的先验信息,因而在较低信噪比以及较低快拍数下对周期平稳信号均能有较好的检测性能。文中仿真实验表明,本文所提算法估计出的伪循环谱相比传统方法估计出的循环谱更为平滑,在相同快拍和信噪比条件下,检测概率均高于传统方法,特别在低信噪比下对检测概率的改善更为明显。 相似文献
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A high accuracy frequency synchronization method is proposed for the 3rd generation partnership project (3GPP) long term evolution advanced (LTE-A) downlink receiver in time division duplexing (TDD) mo... 相似文献
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为了提高频谱感知性能,提出一种基于自相关矩阵行列式的频谱感知新方法,通过授权信号与噪声信号行列式的不同构建统计量。该方法无需信号先验信息、噪声功率信息与精确同步,是一种适用性更强的盲感知算法。仿真表明,与能量检测算法相比,该方法能够取得更好的检测率,且具有复杂度低、不受噪声不确定性影响等优点。 相似文献
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在研究通信信号的循环平稳理论的基础上,将循环谱和峭度相结合,给出了一种低信噪比情况下的信号检测方法,降低了一般循环谱检测方法的复杂度,提高了检测性能。该方法首先采用频域平滑方法计算循环谱的 截面,有效降低背景噪声的影响;然后计算该截面幅度的峭度值 ,当该峭度值 大于门限时,表明检测到信号。仿真结果表明,基于循环谱峭度的信号检测方法优于前人提出的循环谱检测方法,可用于低信噪比情况下的信号检测。 相似文献
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Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method. 相似文献
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Shibing Zhang Xiaodai Dong Zhihua Bao Haoye Zhang 《Wireless Personal Communications》2013,68(3):789-810
Energy detection is a simple spectrum sensing technique that compares the energy in the received signal with a threshold to determine whether a primary user signal is present or not. Setting the threshold is very important to the performance of the spectrum sensing. This paper proposes an adaptive spectrum sensing algorithm where an optimal decision threshold of energy detection is derived based on minimizing the weighted sum of probabilities of detection and false alarm. Since the optimal decision threshold is dependent on the noise power and signal power, a simple, practical frequency domain approach is devised to estimate both. The algorithm can be used for the detection of various kinds of signals without any prior knowledge of the signal, channel or noise power, and is able to adapt to noise fluctuation. Simulations for detecting narrow-band and wideband signals (phase shift keying signal, frequency shift keying signal, orthogonal frequency division multiplexing signal) and ultra-wideband (UWB) signals (direct sequence spread spectrum signals) in an IEEE 802.15.3a UWB band are presented. The results show that the proposed algorithm has excellent robustness to noise uncertainty and outperforms the existing spectrum sensing algorithms in the literature. 相似文献
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低采样率的宽带功率谱估计在很多领域具有应用价值.采用压缩多核采样结构得到信号的压缩测量值, 然后建立测量值相关函数与信号相关函数之间的关系, 用最小二乘法实现相关函数估计, 最后实现功率谱的估计.该压缩采样方法的等效采样率为M/N·fs, 可在没有任何对时域或频域稀疏性的假设条件下降低采样率.仿真分析表明, 该方法的系统噪声与加性噪声性能比周期图法略有降低, 但只要系统设计合理, 对于一定信噪比的信号, 系统噪声与加性噪声基本可以忽略, 并给出了对应的理论分析.估计分辨率与周期图法相比, 等效长度相同时略有提高; 由于本文方法降低了测量值的数目, 对于一定长度的数据来说, 估计分辨率得到了极大的提高.本文方法适用于低信噪比信号的低采样率高分辨率功率谱估计. 相似文献
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Orthogonal frequency-division multiplexing (OFDM), with the help of a cyclic prefix, enables low complexity frequency domain equalization, but suffers from a high crest factor. Single carrier with cyclic prefix (SC-CP) has the same advantage with similar performance, but with a lower crest factor and enhanced robustness to phase noise. The cyclic prefix is overhead, so we put more information in it by implementing this cyclic prefix as a training sequence (TS). This new training aided frequency domain equalized single carrier (TASC) scheme offers us additional known symbols and enables better synchronization and (potentially) channel estimation, with the same performance as SC-CP 相似文献