Wideband spectrum sensing using step-sampling based on the multipath nyquist folding receiver

Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing,which is subject to the complexity of processing the modulated outputs.In this case,a multipath NYFR architecture with a step-sampling rate for the different paths is proposed.The different numbers of digital channels for each path are designed based on the Chinese remainder theorem(CRT).Then,the detectable frequency range is divided into multiple frequency grids,and the Nyquist zone(NZ) of the input can be obtained by sensing these grids.Thus,high-precision parameter estimation is performed by utilizing the NYFR characteristics.Compared with the existing methods,the scheme proposed in this paper overcomes the challenge of NZ estimation,information damage,many computations,low accuracy,and high false alarm probability.Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON SEQUENTIAL COMPRESSED SENSING

Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a priori. This paper presents a cooperative sensing scheme based on se-quential compressed sensing where sequential measurements are collected from the analog-to-information converters. A novel cooperative compressed sensing recovery algorithm named Simul-taneous Sparsity Adaptive Matching Pursuit (SSAMP) is utilized for sequential compressed sensing in order to estimate the reconstruction errors and determinate the minimal number of required meas-urements. Once the fusion center obtains enough measurements, the reconstruction spectrum sparse vectors are then used to make a decision on spectrum occupancy. Simulations corroborate the effec-tiveness of the estimation and sensing performance of our cooperative scheme. Meanwhile, the per-formance of SSAMP and Simultaneous Orthogonal Matching Pursuit (SOMP) is evaluated by Mean-Square estimation Errors (MSE) and sensing time.

AN ADAPTIVE MEASUREMENT SCHEME BASED ON COMPRESSED SENSING FOR WIDEBAND SPECTRUM DETECTION IN COGNITIVE WSN

An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.

Compressive Wideband Spectrum Sensing Based on Random Matrix Theory

Spectrum sensing in a wideband regime for cognitive radio network(CRN) faces considerably technical challenge due to the constraints on analog-to-digital converters(ADCs).To solve this problem,an eigenvalue-based compressive wideband spectrum sensing(ECWSS) scheme using random matrix theory(RMT) was proposed in this paper.The ECWSS directly utilized the compressive measurements based on compressive sampling(CS) theory to perform wideband spectrum sensing without requiring signal recovery,which could greatly reduce computational complexity and data acquisition burden.In the ECWSS,to alleviate the communication overhead of secondary user(SU),the sensors around SU carried out compressive sampling at the sub-Nyquist rate instead of SU.Furthermore,the exact probability density function of extreme eigenvalues was used to set the threshold.Theoretical analyses and simulation results show that compared with the existing eigenvalue-based sensing schemes,the ECWSS has much lower computational complexity and cost with no significant detection performance degradation.

Sub-Nyquist sampling-based wideband spectrum sensing:a compressed power spectrum estimation approach

In this paper,we introduce a sub-Nyquist sampling-based receiver architecture and method for wideband spectrum sensing.Instead of recovering the original wideband analog signal,the proposed method aims to directly reconstruct the power spectrum of the wideband analog signal from sub-Nyquist samples.Note that power spectrum alone is sufficient for wideband spectrum sensing.Since only the covariance matrix of the wideband signal is needed,the proposed method,unlike compressed sensing-based methods,does not need to impose any sparsity requirement on the frequency domain.The proposed method is based on a multi-coset sampling architecture.By exploiting the inherent sampling structure,a fast compressed power spectrum estimation method whose primary computational task consists of fast Fourier transform(FFT)is proposed.Simulation results are presented to show the effectiveness of the proposed method.

Non-reconstruction compressive detector for wideband spectrum sensing based on GLRT

Spectrum sensing is an essential ability to detect spectral holes in cognitive radio( CR) networks. The critical challenge to spectrum sensing in the wideband frequency range is how to sense quickly and accurately. Compressive sensing( CS) theory can be employed to detect signals from a small set of non-adaptive,linear measurements without fully recovering the signal. However,the existing compressive detectors can only detect some known deterministic signals and it is not suitable for the time-varying amplitude signal,such as spectrum sensing signals in CR networks. First,a model of signal detect is proposed by utilizing compressive sampling without signal recovery,and then the generalized likelihood ratio test( GLRT) detection algorithm of the time-varying amplitude signal is derived in detail. Finally, the theoretical detection performance bound and the computation complexity are analyzed. The comparison between the theory and simulation results of signal detection performance over Rayleigh and Rician channel demonstrates the validity of the performance bound. Compared with the reconstructed spectrum sensing detection algorithm,the proposed algorithm greatly reduces the data volume and algorithm complexity for the signal with random amplitudes.

自适应压缩频谱感知的采样率动态调整策略

受奈奎斯特-香农采样定理的限制,宽带频谱感知(wideband spectrum sensing,WBSS)的首要难点是采集宽带信号。根据变步长自适应滤波算法原理,基于自适应压缩频谱感知(adaptive compressed spectrum sensing,ACSS)提出了一种动态调整采样率的压缩频谱感知方法(dynamically adjust sampling rate ACSS,DASR-ACSS)。采用调制宽带转换器结构,针对传统ACSS固定步长的问题设计了步长动态调整策略。针对原始信号难以获取的问题,通过对信道占用状态相关性和检测率的变化趋势进行数据统计分析,设计了利用信道占用状态相关性的停止准则;通过对停止准则的实验数据分析,以sigmoid函数为基础设计了分段函数作为步长调整策略,利用步长补偿因子充分提升性能。仿真结果表明,与传统ACSS相比,DASR-ACSS能更快达到高检测率,能够平衡算法的实时性能和采样率压缩比性能,当信噪比在-30~30 dB时,DASR-ACSS表现出更好的检测性能。

Joint compressive spectrum sensing scheme in wideband cognitive radio networks

In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use parallel low rate analog to digital conversions（ADCs） and fewer storage units for wideband spectrum signal sampling.The proposed scheme uses multiple low rate congitive radios（CRs） collecting compressed samples through AICs distritbutedly and recover the signal spectrum jointly.A general joint sparsity model is defined in this scenario,along with a universal recovery algorithm based on simultaneous orthogonal matching pursuit（S-OMP）.Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.

高频率分辨的金刚石氮-空位色心宽频谱成像技术

高分辨率宽频谱测量技术在天文学、无线通信、医学成像等领域具有重要应用价值.金刚石氮-空位(nitrogen-vacancy,NV)色心因其高稳定性、高灵敏度、实时监测、单点探测以及适用于长时间测量等特性已成为频谱分析仪备受关注的选择.目前,基于NV色心作为探测器的宽频谱分析仪能够在几十GHz频带内进行实时频谱分析,然而其频率分辨率仅达到MHz水平.本文通过搭建结合连续外差技术的量子金刚石微波频谱成像系统,利用磁场梯度对NV色心谐振频率进行空间编码,成功获取了900 MHz—6.0 GHz范围内完整的频谱数据.在可测频谱范围内,系统进一步采用连续外差的方法,同时施加谐振微波和轻微失谐的辅助微波对NV色心进行有效激发,增强了NV磁强计对微弱微波信号的响应.该方法使系统在可测频谱范围内实现了1 Hz的频率分辨率,并能够对间隔为1 MHz扫频步进的多个频点的频率分辨率进行单独测量.以上研究结果表明基于NV色心的宽频谱测量可实现Hz级频率分辨,为未来的频谱分析和应用提供了有力的技术支持.

基于声学宽带扩频信号的抗多径测距技术研究

为研究宽带扩频信号在复杂水声信道下的测距精度,提高水声定位系统的抗多径性能,以BELLHOP模型的多径干扰冲激响应作为水声信道环境基础,通过构造不同传播距离、海洋环境特征的多径水声信道,对宽带扩频编码进行同步相关测距仿真;提出一种基于宽带扩频信号载波域同步的抗多径测距方法,利用载波携带的宽带特征信息进行相关同步以提高理论测距精度。实验结果表明:在有直达声且不考虑声线弯曲条件下,浅海多径信道条件测距精度可达厘米级,中深水信道维持分米级测距精度,较基带同步方法的抗干扰性能有所提升,基于宽带扩频信号载波域同步的抗多径测距方法,能够为声学定位系统的高性能测距技术提供理论依据。

A two-stage spectrum sensing scheme based on energy detection and a novel multitaper method

Wideband spectrum sensing has drawn much attention in recent years since it provides more opportunities to the secondary users. However, wideband spectrum sensing requires a long time and a complex mechanism at the sensing terminal. A two-stage wideband spectrum sensing scheme is considered to proceed spectrum sensing with low time consumption and high performance to tackle this predicament. In this scheme, a novel multitaper spectrum sensing （MSS） method is proposed to mitigate the poor performance of energy detection （ED） in the low signal-to-noise ratio （SNR） region. The closed-form expression of the decision threshold is derived based on the Neyman-Pearson criterion and the probability of detection in the Rayleigh fading channel is analyzed. An optimization problem is formulated to maximize the probability of detection of the proposed two-stage scheme and the average sensing time of the two-stage scheme is analyzed. Numerical results validate the efficiency of MSS and show that the two-stage spectrum sensing scheme enjoys higher performance in the low SNR region and lower time cost in the high SNR region than the single-stage scheme.

基于Radon变换的宽带信号多域参数同时测量方法

实际电子监测设备的作战环境复杂多变,辐射源个数、工作频率、工作带宽、到达角度等多域参数信息对于实现工作场景的实时精确感知至关重要。提出一种基于Radon变换的宽带信号多域参数同时测量方法,实现多个宽带信号多域重点工作参数的同时快速测量,大大提升低信噪比条件下监测目标特性的多维实时感知能力。该方法结合数字信道化结构及二重FFT算法实现全侦收频段及全侦收角度的空时域联合功率聚焦,并生成作战场景的空时二维频谱幅度图,利用Radon变换算法实现不同映射角度的线性积分运算,构建合适的检测门限,推导计算全场景多目标多参数测量结果。实验显示,该方法可以有效提升弱信号的参数测量精度,实现多目标多域参数同时测量。

高区分度欠采样宽带谱分析器设计

为克服现有基于多陪集、调制宽带转化器等欠采样结构的谱分析器存在谱成分区分度差、欠采样通道数多、有效探测带宽窄的严重缺陷,提出基于全相位滤波器对半分解的互素欠采样谱分析器设计.首先,剖析了经典互素谱分析存在伪峰效应的原因.然后,具体提出基于最小尺寸的全相位滤波器对半分解的解析方法设计互素谱原型滤波器,并从中衍生出两路互素谱分析结构并行的谱分析器,从而提高了谱区分度;分析了两路并行互素谱输出的频点位置关系,通过对两路输出做交叉合并,生成双倍频谱分辨率的全景谱,既可消除伪峰,又无需增加欠采样通道数(即为最小值2);在全景谱重构计算中,通过将上、下通道支路的互相关扫描代替现有欠采样谱分析的压缩感知求解计算,绕过了对信源带宽稀疏度的要求,从而不仅拓宽有效探测带宽,还获得了密集谱区分能力.最后分别以多子带的数字已调信号、线性调频信号为信号模型,对所提出的高区分度宽带谱分析器的谱估计效果进行验证.仿真结果表明,该谱分析器可以消除现有互素谱分析方法存在的伪峰效应,且可在总体欠采样率不足0.5倍调制宽带转化器欠采样率的条件下,可区分的谱成分数目提升至其2.5倍以上.在跨频段、超分辨率的欠采样频谱感知中具有广阔的应用前景.

50GHz~110GHz宽带信号下变频技术

矢量信号分析仪作为传统扫频式频谱仪与快速傅里叶变换技术的结合,能够对信号进行数字化处理,分析各种调制信号,同时获得测量信号的幅度和相位信息,对于现代通信意义重大。随着电磁波的应用频率扩展到毫米波乃至太赫兹波段,矢量信号分析仪的测量频率也急需拓展到更高频段,在系统前端搭载下变频器可实现这一目的。基于50GHz~110GHz宽带信号的下变频技术开展研究,研制了变频系统中多个关键器件,包括频分50GHz~110GHz信号的宽带开关、抑制镜频信号的宽阻带波导滤波器以及基于倍频放大模块的本振,同时搭建了50GHz~110GHz宽带信号下变频系统,将50GHz~110GHz宽带信号下变频到4.6GHz~39.6GHz。实验表明,下变频后的信号在任意2GHz内平坦度小于±3.5dB,对镜频信号的抑制大于60dBc,输出杂散抑制度大于40dBc,本振泄漏抑制大于70dBc,满足系统指标要求。

Energy-Based Collaborative Spectrum Sensing for Cognitive UWB Impulse Radio

This paper focuses on the issue of collaborative spectrum sensing in cognitive ultra wideband（CUWB） impulse radio. We employ energy-based signal detection method and apply the Neyman-Pearson（NP） decision rule to determine the optimum threshold. Two cooperative spectrum sensing schemes are developed in this paper. The decision fusion scheme is based on hard decision, in which each cooperating cognitive user（CU） sends its own local decision to the fusion center（FC）. The FC then makes a final decision according to the majority voting rule. Alternatively, the data fusion scheme is based on soft decision, in which each local CU sends its observed value directly to the FC. The FC combines these values, compares to a threshold and then makes the final decision. The performances of both schemes are studied by using analytical tools and computer simulations. The receiver operating characteristics（ROC）, which reveal the probability of detection versus false-alarm curve, are employed to evaluate the system performance under different scenarios. Simulation results demonstrate that the data fusion scheme outperforms the decision fusion scheme and verify that the collaborative spectrum sensing has practical importance in CUWB networks.

基于PSO-LSSVM的非重构宽带压缩频谱感知方法

频谱感知是认知无线电的关键技术之一,而宽带环境使采样设备压力大,宽带压缩频谱感知有效地解决了此问题,但压缩重构造成巨大计算复杂度,并且传统检测方法受门限限制,在低信噪比下检测率低。提出将机器学习与非重构压缩感知技术应用到频谱感知当中的方法,原始信号经压缩感知得到测量值,对其进行采样协方差预处理后,通过改进PSO-LSSVM算法对数据进行训练分类,解决门限困扰,并适用于宽带频谱感知。仿真结果表明,此方法在低信噪比下,与传统检测相比有较好的检测结果。

基于频域叠加和深度学习的频谱信号识别

针对频谱监测领域频带较宽、接收机采样步进受限而造成的宽带频谱信号难以识别的问题,提出了一种频域叠加预处理和目标检测相结合的频谱信号识别方法。该方法利用频域叠加对频谱数据进行多帧叠加来突出频谱中的弱信号,将叠加处理后的频谱图像送入改进的目标检测网络中进行信号类型的识别。经过实验证明,此方法能够有效地识别7种类型的频谱信号,提出的频域叠加预处理能够提升目标检测算法的精度,并提高频谱中弱信号的识别能力,在信噪比为6 dB时,算法能够达到89.7%的平均识别率。

基于分布式宽带调制转换器的协作频谱感知

针对分布式宽带调制转换器在感知节点数满足信号稀疏后检测成功概率趋于平稳,而通过增加感知节点数并不能有效提升检测成功概率且复杂度更高的问题,提出了一种基于分布式宽带调制转换器的协作频谱感知方案。该方案采用一种基于认知用户组检测的协作频谱感知结构,根据信号占用子信道数K计算组内感知节点数并进行分组,组内感知节点采用分布式调制宽带转换器方案感知宽带频谱信号的支撑集;组间采用区域映射融合模型的硬判决方案将不同认知用户组支撑集进行融合,区域映射融合模型根据贪婪算法中内积选择原子的顺序将子信道进行区域映射。仿真结果表明,与基于分布式调制转换器频谱感知算法相比,该算法有效提高了频谱检测成功概率并降低了算法复杂度。

一种采用压缩协方差的无线电宽带频谱感知方法

为有效降低宽带频谱感知的观测时间和计算复杂度,提出了一种基于压缩协方差的无线电宽带频谱感知方法。首先,通过循环稀疏规则测量不同标记的距离,运用多陪集采样组代替奈奎斯特模数转换器,形成基于多陪集采样库的欠奈奎斯特采样结构;其次,构建压缩协方差频谱感知模型,运用频谱决策模块对输入样本进行处理,完成频谱分析;最后,通过Matlab生成测试信号数据,对所提频谱感知算法进行建模与性能分析。实验结果表明,所提方法能够将检测误差控制在有效范围内,且与传统频谱检测方法相比,所提方法在不同信噪比环境下具有更高的频谱检测水平度。