A Modified CORDIC FPGA Implementation for Wave Generation

This paper presents a modified coordinate rotation digital computer (CORDIC) algorithm implemented in parallel architecture to generate sine and cosine waveform. Since CORDIC is a combination of only additions and shifts, it can be efficiently implemented in hardware. The proposed algorithm further approximates the way of computing rotation angle based on Taylor series in order to reduce the usage of Read-Only-Memory (ROM) table. Thus area and power is reduced due to partial usage of ROM storage. The precision remains the same as the original algorithm. The modified 32-bits pipeline CORDIC are implemented in Spartan XC3S500E device using Xilinx ISE 12.3 design suite. The result is compared with original CORDIC and Xilinx coregen in device utilization. It is shown that the logic usage is 31 FFs and 285 FFs less than the original design and Xilinx core, respectively. When compared with the original design, the signal power and total power reduction at 40 MHz clocks are 7.69 % and 1.35 %, respectively. The bit error remains at 10^?8 dB level. The SNR of modified CORDIC is about 2 dB lower, which is acceptable in wave generation.     

MME频谱感知算法能量效率优化分析

绿色通信是未来无线通信发展的必然趋势,为了提高认知无线网络频谱感知算法的能量效率以及算法稳定性,实现低功耗绿色通信,该文在噪声不确定的实际环境下分析了基于特征值的最大最小特征值（MME, Maximum-Minimum Eigenvalue）频谱感知算法的能量效率,并利用黄金分割优化算法获得了使能量效率最大化的最优感知时间。理论分析和仿真结果表明,在噪声不确定的情况下,基于特征值的MME感知算法的能量效率要明显优于传统的能量检测算法,且MME感知算法不受噪声不确定性的影响,在低信噪比下具有更稳定的感知性能。      

低信噪比条件下基于随机共振的感知方法与性能分析

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.     

基于随机共振和非中心F分布的频谱感知算法

为解决频谱感知算法在低信噪比（SNR）时检测概率较低且检测所需采样点数较多的问题,提出了基于随机共振和非中心F分布（SRNF）的频谱感知算法。通过引入直流随机共振噪声,建立了SRNF的系统模型,推导了服从非中心F分布的检验统计量表达式、虚警概率与检测概率以及判决门限表达式,并采用数值法求解最佳的随机共振噪声参数。仿真结果表明,在低信噪比时,所提基于SRNF算法的检测性能优于能量检测（ED）算法和基于F分布的盲频谱感知（BSF）算法,当虚警概率为5%、信噪比为–12 d B、采样点数为200时,所提算法的检测概率是95%,分别比BSF算法和ED算法高34%和67%;当信噪比为–12 dB、检测概率达到95%时,所提算法所需的采样点数是210,比BSF算法节省了340个采样点。此外,噪声不确定度对所提算法的影响小于ED算法。     

Sparse Representation for Blind Spectrum Sensing in Cognitive Radio: A Compressed Sensing Approach

Cognitive radios enable opportunistic transmission for secondary users (SUs) without interfering the primary user (PU). Cyclo-stationary-based spectrum sensing methods are better than the energy detection methods in negative signal-to-noise (SNR) decibel (dB) regime, in which case the noise variance cannot be exactly estimated. However, blind cyclo-stationary methods require a large number of symbols (and hence measurements). This paper aims to reduce the number of measurements in a blind sensing method (using a combination of linear prediction and QR decomposition), by employing compressed sensing at the receiver front-end, so as to reduce the A/D requirements needed with a large number of measurements, along with oversampling the received signal. Till now, compressed sensing has not been investigated at very low negative SNR (dB), e.g., \(-\)12 dB, which is very crucial in spectrum sensing. The novel algorithm, in this paper, overcomes this shortcoming, and its simulation results show that the SU is able to detect the PU signal, using much less measurements, even at very low negative SNR (dB). The proposed method also investigates the effect of joint and individual measurement matrices at multiple oversampled branches.     

基于多通道的跳频信号欠采样频率估计

为实现跳频信号频率跟踪估计,本文提出一种基于多通道的跳频信号欠采样频率估计方法。基于快速傅里叶变换（FFT）,提出了一种3谱线方程的频率校正算法,提高了基于中国余数定理的频率估计方法对短序列信号的频率估计精度,与现有的两种基于离散傅里叶变换（DFT）的频率校正算法相比,序列补零数量灵活。给出了一种频率估计检错机制,可以提高算法可靠性。仿真结果表明,本文所提频率估计算法的精度优于现有算法,增加序列补零数量可进一步提高算法的估计精度和信噪比阈值,降低误差平台;检错机制在-23 dB至8 dB信噪比范围内的准确率高于95.5%。      

Cooperative Spectrum Sensing with Adaptive Double-Threshold Based Energy Detector in Cognitive Radio Networks

Spectrum sensing is one of the key functionalities in the implementation of cognitive radio. It is used to sense the unused spectrum in an opportunistic manner. In this paper, we propose an energy detector with adaptive double-threshold for spectrum sensing, to optimize the detection performance at a fixed probability of false alarm $(\text{ P }_\mathrm{f})$ i.e. 0.1, which also overcomes sensing failure problem. In the present work, the detection threshold is made adaptive to the fluctuation of the received signal power in each local detector of cognitive radio (CR) user. Simulation results show that proposed scheme optimizes better detection performance and outperforms both conventional energy detector and cooperative spectrum sensing (CSS) method by 12.8 and 3.3 % at $-$ 8 dB signal to noise ratio (SNR), respectively. While utilizing CSS with proposed adaptive double-threshold scheme, where each CR user use a double threshold detectors for local detection and send detection decisions to fusion center (FC) to give the final decision based on hard decision rule. It is further found that CSS with adaptive double-threshold improves detection performance around 26.8 and 7.6 % as compare to CSS with single threshold and Hierarchical with quantization method at $-$ 10 dB SNR, respectively, under the case when a small number of sensing nodes are used in spectrum sensing.     

An Efficient Adaptive Filter Architecture for Improving the Seizure Detection in EEG Signal

This paper presents efficient low-power adaptive filter architecture for electroencephalogram (EEG) signal and epileptic seizure detection using recurrence quantification analysis (RQA). The preprocessing of EEG is done using notch, wavelet and adaptive filter. The comparison of signal-to-noise ratio of the filter outputs proves that the adaptive filter provides better performance and a parallel interleaved sample of direct form adaptive FIR filter architecture is implemented and low-power issues are addressed. An innovative compressor-based addition technique is utilized in the filter implementation to reduce the area and power consumption. The design is developed using Verilog HDL and mapped to 65-nm technological node. The power results are compared with conventional architecture of adaptive filter. The major advantage of choosing RQA is that it provides better information even for short non-stationary and nonlinear signals where other methods fail to provide good results. And it requires no conventions about data set size or dispersal of the data. The algorithm is applied on epileptic EEG signal from CHB-MIT database. The RQA measures are determined from the recurrence plot, its performance is measured in terms of sensitivity and specificity as 97.4 and 93.5 %, respectively, and leakage power is reduced to 10 %.     

基于信号能量分布拟合优度的长短时记忆网络频谱感知算法研究

传统频谱感知算法性能在低信噪比下不够理想,在高信噪比下较好,算法性能随信噪比降低逐渐变差。本文提出了基于信号能量分布拟合优度的长短时记忆网络频谱感知算法,利用授权用户信号存在时的接收信号为基础,计算接收信号的能量分布,并将通过拟合优度算法得到的距离值作为特征构造特征向量,然后将特征向量输入长短时记忆网络训练得到模型,最后将测试数据输入训练模型进行预测,从而实现频谱感知。仿真结果表明,本文提出的新算法在信噪比为-13 dB,采样点数为28时,检测概率达到96.21%,明显优于传统能量检测算法和传统拟合优度算法。      

A 2.1 μW 80 dB SNR DT ΔΣ modulator for medical implant devices in 65 nm CMOS

This paper presents a simple and robust low-power ΔΣ modulator for accurate ADCs in implantable cardiac rhythm management devices such as pacemakers. Taking advantage of the very low signal bandwidth of 500 Hz which enables high oversampling ratio, the objective is to obtain high SNDR and low power consumption, while limiting the complexity of the modulator to a second-order architecture. Significant power reduction is achieved by utilizing a two-stage load-compensated OTA as well as the low-V_T devices in analog circuits and switches, allowing the modulator to operate at 0.9 V supply. Fabricated in a 65 nm CMOS technology, the modulator achieves 80 dB peak SNR and 76 dB peak SNDR over a 500 Hz signal bandwidth. With a power consumption of 2.1 μW, the modulator obtains 0.4 pJ/step FOM. To the authors’ knowledge, this is the lowest reported FOM, compared to the previously reported second-order modulators for such low-speed applications. The achieved FOM is also comparable to the best reported results from the higher-order ΔΣ modulators.     

四分法和差分相干结合的北斗弱信号捕获算法

针对弱信号条件下传统卫星信号捕获算法无法满足用户需求的问题,在对接收信号进行傅里叶变换的基础上,提出了一种利用四分法估计导航数据比特跳变位和差分相干累加方法相结合的北斗弱信号捕获新算法。与基于FFT的半比特交替和相干累加结合的捕获方法比较可知,该算法数据利用率高,对噪声有良好的抑制效果。仿真结果表明,该算法可实现-38 dB信噪比条件下的北斗弱信号捕获,并可进一步提高软件接收机灵敏度。     

基于LSTM神经网络的频谱感知算法

在频谱感知中经典的能量检测算法在低信噪比时检测性能较低且门限难以估计,基于机器学习的感知算法受限于检验统计量的构造会造成接收信号原有结构信息的丢失。针对这些问题,本文提出一种基于LSTM神经网络的频谱感知方法,首先利用接收信号序列作为神经网络的输入特征向量,然后使用LSTM神经网络进行训练得到分类器,最后使用训练好的模型实现频谱感知。该方法无需估计检测门限值,也无需构造特征向量,仿真结果表明,所提算法在采样点和次级用户更少的情况下仍优于对比算法。      

An 8-bit 208 MS/s SAR ADC in 65 nm CMOS

An 8-bit low-power 208MS/s SAR analog-to-digital converter is presented. To achieve a high-speed and low-power operation, a reused terminating capacitor switching procedure is proposed. The proposed switching procedure halves the capacitors leading to a significant power saving over the conventional one. Moreover, the proposed architecture relaxes the settling time of DAC and subsequently improves the conversion rate. The ADC has been simulated in SMIC 65 nm 1.2 V CMOS technology. At a 1.2-V supply and 208 MS/s, the ADC consumes 2.7 mW and achieves an SNDR of 49.6 dB, an SFDR of 61.0 dB with 100 MHz inputs.     

基于多通道数据融合的跳频信号频率跳变时刻估计算法

针对低信噪比条件下宽带高速跳频信号的参数估计问题,提出一种多通道数据融合跳频信号频率跳变时刻估计算法。该算法利用无盲区数字信道化预处理实现宽带跳频信号的全概率、全盲接收,通过数据融合得到一路包含全部频率跳变信息的参考信号,并利用该参考信号进行最大似然估计得到频率跳变时刻精确估计值。给出了所提出跳变时刻估计算法的Cramer-Rao下界并进行实验仿真。理论推导和仿真结果表明:针对一定的输入信噪比,适当选择信道化数和估计时间可实现较高的估计精度。在0dB信噪比条件下,采用16通道数字信道化处理,估计方差小于10-3。      

基于稀疏系数位置和归一化残差的压缩感知信号检测

信号检测是压缩感知理论研究的重要内容。针对当前压缩感知信号检测算法没有充分利用稀疏系数幅值和位置信息的不足,提出了一种新的检测算法。该算法首先引入归一化残差变量,有效克服了稀疏系数幅值波动大的缺点;然后,利用不同测量矩阵确定的稀疏系数位置信息,基于正交匹配追踪( OMP)算法实现目标信号检测。实验结果表明,算法的检测性能随着信噪比的提高而增强,且与压缩比负相关,运算复杂度较正交匹配追踪算法和仅利用稀疏系数位置信息的算法相当但检测性能分别提高了4 dB和1 dB。     

一种负信噪比下超宽带信号检测的新方法

提出了利用分段相关平均法进行极低信噪比下窄脉冲检测的新方法,并讨论了该方法的理论极限性能和算法增益原理。理论分析和仿真表明本算法能检测SNR为-12dB的超宽带信号。为了获得更低的检测SNR,又采用小波变换对信号进行消噪,接着再进行分段相关平均的方法,使可检测SNR达到-17dB。     

基于非参量CUSUM的认知超宽带频谱感知算法

提出了一种基于非参量CUSUM的超宽带信号快速检测算法,该算法针对认知超宽带频谱感知中超宽带信号检测问题,根据超宽带信号占空比低的特点,选择短时能量作为非参量CUSUM算法的观测统计量,将块检测思想和序列检测思想进行有机结合,克服了块检测算法的信噪比门限效应,并采用加滑动预处理窗的方式降低噪声对观测统计量的影响。本文通过自适应改变非参量CUSUM算法中Reference Value取值的方法,提出改进算法以进一步缩短检测延迟。仿真证明,占空比为1/10、信噪比为-25dB时, 所提算法可在用户发送50个脉冲的时间内以90%的概率检测出主用户信号的存在,而能量检测算法在8dB时才能达到相同检测概率,且所提算法检测延迟小于能量检测算法。改进算法在-10dB≤SNR≤5dB条件下可明显缩短检测延迟。      

利用卷积神经网络和协方差的协作频谱感知算法

针对基于信号协方差矩阵的频谱感知算法门限难于准确得到及没有充分利用原始信号信息等问题,提出了基于卷积神经网络和协方差矩阵的协作频谱感知算法。首先将接收的I、Q两路正交信号的归一化协方差矩阵组成双通道输入矩阵,然后使用卷积神经网络直接提取协方差矩阵的特征信息,并进行训练得到分类器,最后使用训练好的模型进行频谱感知。仿真结果表明,本文所提出的频谱感知算法优于对比算法,在信噪比为-13 dB、40个次用户协作感知时,本文算法虚警概率低于0.1,检测概率达到0.9以上。      

基于先验信噪比估计的超宽带穿墙雷达呼吸信号检测算法研究

废墟下呼吸信号的检测对地震救援具有重要意义.在实际中,障碍物(如墙体)后的人体呼吸信号会被环境中的噪声所掩盖.如何提升穿墙呼吸信号的信噪比(SNR)仍是一项具有挑战性的工作.该文提出一种基于先验信噪比估计的检测算法,用于增强穿墙弱呼吸信号的输出SNR.该算法在谱减法中典型的决策导向(DD)算法基础上加入了自适应权重因子...     

基于检测门限优化的DVB-T信号联合检测算法

针对检测过程中认知信号检测门限难以合理设定以及检测准确率不高的问题,根据数字电视地面广播(DVB-T)信号循环前缀特征,给出一种联合能量检测与循环前缀相关检测算法。该算法将虚警概率与检测概率的加权作为门限的函数,通过最小化函数获得优化检测门限,最终达到提高系统检测性能的目的。仿真结果也表明,在信噪比为-25~-10 dB时,对比传统能量检测算法,采用优化判决门限的能量检测算法,检测概率提高了10%~22%,联合检测算法的检测准确率提高了2%~25%。