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基于最佳中继选择的协作频谱感知方案研究 总被引:8,自引:0,他引:8
本文提出了一种基于最佳中继的多用户协作频谱感知方案,通过认知无线电网络中多用户间的相互协作,可以获得明显的空间分集增益,从而改善认知用户的检测性能.针对所提出的多用户协作感知方案,在瑞利衰落环境下分析了相应的系统检测概率,同时也理论推导了传统非协作方案的感知性能.根据检测概率的解析式,对非协作方案和多用户协作方案,进行了相应的数值实验和性能比较.与非协作感知方案相比,多用户协作方案能够显著提高主用户的检测概率.此外,随着候选中继用户数目增加,多用户协作方案对主用户检测概率的改善量越加明显. 相似文献
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认知无线电网络的一种协作频谱感知方案 总被引:3,自引:3,他引:0
认知无线电技术能够让非授权用户利用已经分配给授权用户的频段.为了不对首要用户的工作造成干扰,认知用户需要对频谱进行不间断的监测来判断首要用户是否存在.因此,频谱的感知是认知无线电技术的关键.协作频谱感知能够充分的利用网络资源,提高网络中的认知用户的检测概率.文中笔者简单地介绍了一种协作频谱感知的方案.仿真结果表明,通过该方法能够提高网络中认知用户的检测概率,提高网络的检测灵敏度. 相似文献
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无线网络中利用协作分集技术可显著提高传输性能。认知无线网络中采用时间空间联合频谱检测能更充分地利用主用户的空闲频谱空洞。本文对采用时间空间联合频谱检测的认知协作分集系统的中断概率性能进行分析,给出采用选择解码转发中继协议时在瑞利衰落信道下的准确中断概率闭合式。分析和仿真结果表明:采用时间空间联合频谱检测的系统的中断概率性能优于单独时间频谱检测和单独空间频谱检测的系统,而协作分集技术的引入也明显改善了认知用户的传输性能。中断概率的理论结果对于衡量和评估认知网络的频谱检测方案、协作传输方案的性能有重要的理论和实用价值。 相似文献
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频谱感知是认知无线电系统中的关键技术。本文将群智感知的激励机制与协作频谱感知有效结合,提出了一种基于多任务群智感知的协作频谱感知算法。该算法考虑检测概率和感知时间建立了参与感知的次用户效用函数,次用户通过优化感知时间得出次用户的最优效用并确定感知的信道,通过贪婪算法在有限预算限制下来选取参与感知的次用户,在次用户感知完成后发放一定的报酬,以激励次用户参与频谱感知的积极性。仿真结果表明,该算法可以同时感知多个信道,通过激励提高了次用户的参与度,使得协作频谱检测概率得到有效的提升。 相似文献
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在Femtocell和Macrocell构成的两层异构网络中,前人提出运用认知无线电的技术来解决网络中存在的干扰问题,但大多数研究主要集中在频谱资源的管理上,没有提出高效的频谱检测方法。本文结合认知无线电中频谱感知技术提出了基于双门限的两层协作频谱感知,检测出空洞的频谱资源分配给Femtocell用户使用,既能提高频谱资源的利用率,又能有效的抑制Femtocell与Macrocell之间的干扰。文中推导出了Femtocell用户基于双门限的两层频谱感知的检测概率和虚警概率,给出了Femtocell网络中感知信息两比特编码的融合准则和基于双门限的两层协作频谱感知的具体实施方法。仿真结果表明,所提算法能够有效提高Femtocell用户的频谱检测概率,通过对感知信息进行两比特编码,再将编码后的信息发送至融合中心进行融合,能够有效的提高整个系统的检测性能,抗噪声能力强。 相似文献
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Reliable spectrum detection of the primary user (PU) performs an important role in the cognitive radio network since it’s the foundation of other operations. Spectrum sensing and cognitive signal recognition are two key tasks in the development of cognitive radio (CR) technology in both commercial and military applications. However, when the CR terminals receiving signals have little knowledge about the channel or signal types, these two tasks will become much more difficult. In this paper, we propose a reliable cooperative spectrum detection scheme, which combines the cooperative spectrum sensing with distributed cognitive signal recognition. A novel improved cooperative sensing algorithm is achieved by using a credibility weight factor and the “tug-of-war” rule, which is based on the double threshold detection and Dempster–Shafer theory, to determine whether the PU signals exist. In this scheme, cognitive signal recognition can be used to identify the signal type when the PU signal is present. During the cognitive signal recognition processing, the CR terminals make local classification of the received signals by using Daubechies5 wavelet transform and Fractional Fourier Transform, and send their recognition results to the globe decision making center. A distributed processing uses these cognitive terminals’ local results to make final decisions under the Maximum Likelihood estimation algorithm. Simulation results show that the proposed method can achieve good sensing probability and recognition accuracy under the Additive White Gaussian Noise channel. 相似文献
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In cognitive radio (CR) network, to improve spectrum sensing performance to primary user (PU) and decrease energy wastage of secondary user (SU) in cooperative spectrum sensing, an energy harvesting-based weighed cooperative spectrum sensing is proposed in this paper. The SU harvests the radio frequency (RF) energy of the PU signal and then converts the RF energy into the electric energy to supply the power used for energy detection and cooperation. The time switching model and power splitting model are developed to realize the notion. In the time switching model, the SU performs either spectrum sensing or energy harvesting at any time, while in the power splitting model, the received PU signal is split into two signal streams, one for spectrum sensing and the other one for energy harvesting. A joint optimization problem is formulated to maximize the spectrum access probability of the SU by jointly optimizing sensing time, number of cooperative SUs and splitting factor. The simulation results have shown that compared to the traditional cooperative spectrum sensing, the proposed energy harvesting-based weighed cooperative spectrum sensing can decrease the energy wastage obviously while guaranteeing the maximum spectrum access probability. 相似文献
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协同频谱感知器通过充分利用多个认知无线电用户的空间分集增益,对抗单用户深度衰落和阴影效应问题,降低了感知系统对本地感知用户的灵敏度要求,减少由于单用户检测不确定性带来的系统误判。利用D-S方法进行协同频谱感知,通过在本地提取置信指派,再上传至融合中心进行证据推理与判决,占用较窄的控制信道带宽,达到优于传统方法的检测性能,如“或”、“与”和“最优融合”感知方法。但低信噪用户带来的冲突数据会限制D-S方法性能,使其信噪鲁棒性较差。本文首先定义感知用户基本置信指派函数,基于DSmT提出证据折扣优化 DSmT协同频谱感知器。该感知器根据不同认知用户数据的可靠性,对其置信指派函数进行折扣,加强高可靠性数据对融合结果的贡献,降低不可靠数据对融合结果的干扰,有效解决冲突数据下的协同频谱感知信息融合问题。仿真结果表明,证据折扣优化DSmT协同频谱感知器具有良好的检测性能和信噪比鲁棒性。 相似文献
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在基于中继的协作频谱感知中,尽管通过引入认知中继可有效提高协作频谱感知性能,然而认知中继的引入也会带来额外的系统开销及复杂度增加问题。为了节约系统开销,本文在前期取得研究成果的基础上,进一步提出了一种基于删余的最佳中继协作频谱感知方案,只有当次用户检测到主用户信号且目标次用户的报告信道衰落严重时,才申请认知中继的协作传输,同时目标次用户将其检测到的感知信息发送到认知中继;最后,分别从检测性能和次系统可获得的容量角度对所提方案下的协作频谱感知性能进行了理论分析。分析和仿真结果表明,所提方案可以有效提高检测性能,当确保主用户受到足够保护的前提下,利用所提方案可以获得更高的次系统容量。 相似文献
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In this paper a novel multiple-user cooperative spectrum sensing scheme (MCSS) based on hybrid relay is proposed to achieve the spatial diversity gain in detection of the primary user (PU) in a cognitive radio (CR) network. A practically important case where co-channel interference signals are present at the network is considered for the analysis. Closed-form expressions of detection probability \((\hbox {P}_{\mathrm{d}})\) and false alarm probability \((\alpha )\) for the proposed adaptive decode-and-forward based multiple-user cooperative spectrum sensing scheme (ADF-MCSS) using energy detector over Rayleigh fading sensing channels is derived in presence of co-channel interference at the secondary user which is far away from the PU. Further we extend the concept of two user amplify-and-forward (AF) and decode-and-forward (DF) cooperative spectrum sensing schemes in multiple-user scenario (i.e. AF-MCSS and DF-MCSS) over Rayleigh fading channels when the secondary user (which is far away from PU) is affected by interference. Closed-form expressions of AF-MCSS and DF-MCSS schemes over a Rayleigh fading channels are also evaluated and compared with that of proposed ADF-MCSS in presence of interference signals at the secondary user. Further the performance analysis of AF-MCSS, DF-MCSS and ADF-MCSS schemes are compared with the existing non-cooperative spectrum sensing schemes in presence of interference at the secondary user. Our analysis is validated by numerical and simulation results for multiple-user CR network. The impact of number of cooperative relays, SNR in sensing channel, energy of interference signal, false alarm on detection probability in proposed ADF, AF and DF schemes is shown. 相似文献
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Cooperative spectrum sensing has been shown to be an effective approach to improve the detection performance by exploiting the spatial diversity among multiple cognitive nodes. By using the amplify-and-forward relaying with partial relay selection, this paper proposes a novel cooperative spectrum sensing scheme, which provides higher detection performance and is interesting in distributed cognitive radio networks. In the proposed sensing scheme, the “best” cognitive relay by means of partial relay selection technique amplifies and forwards the signals transmitted from the primary user (PU) to the cognitive user (CU). Then the CU detects PU’s states (i.e., presence or absence) via an energy detector. Moreover, the average missed-detection probability of proposed sensing scheme is studied over Nakagami-m fading channels, where m is a positive integer. In particular, the tight closed-form lower bounds of the average missed-detection probability are presented for the convenience of performance evaluation in practice. Finally, numerical results are provided to validate the derived closed-form lower bounds and the influence of the number of cognitive relays on the detection performance is also discussed. 相似文献
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近年来,基于能量检测的协作频谱感知算法被广泛应用于频谱感知领域。由于该方法在计算能量检测的判决门限受噪声影响较大以及受限于认知用户的数量等问题,导致其检测性能受到影响。为了解决这一问题,本文提出一种基于图像K-means聚类分析的频谱感知算法。这种方法利用主用户信号存在与否的两种认知信号状态映射成图像,经过调整图像强度和高斯滤波预处理之后利用提取图像像素分布直方图的方法提取出特征向量,然后利用改进的K均值聚类算法对这些特征向量进行训练得到分类模型。最后利用训练好的分类模型对未知信号进行检测,从而实现频谱感知。仿真结果表明,本文所提出的频谱感知算法,在检测性能上优于传统能量检测以及协作频谱感知算法,尤其在低虚警概率、低信噪比的环境下效果更加突出。 相似文献