基于模糊理论的分布式协作谱感知技术研究

针对认知无线网络中对于主用户检测感知存在的阴影衰落和如何有效提高频谱利用率等问题,提出了一种基于模糊理论的分布式协作处理方法。该方法通过采取各感知用户独立自主决策(双门限+模糊判决区)和综合接收分析一跳邻居感知处理信息等措施,实现认知无线网中各局部区域的自动聚类和自适应聚类边界(/过渡带)识别,从而实现对主用户的全面感知和大幅度地提高频谱利用率。仿真结果表明,该方法在感知处理信息传输开销、认知系统吞吐率、检测概率等方面明显优于传统的合作感知方法。

CR系统谱感知技术研究

认知无线系统中谱感知是基础与前提,以认知无线系统谱感知的研究为主题,在综合相关研究成果的基础上从谱感知研究的核心技术内容入手,着重对有关本地检测的技术手段、先验知识的参考应用、谱预测与主用户网络信息的利用等关键性问题进行了阐述分析,对涉及问题解决的核心要点进行了概括分析,同时在综合考虑认知无线系统工作应用实际的情况下,对今后认知无线系统谱感知研究需要重点关注的方向进行了讨论分析。

以欠采样速率实现盲谱感知及二维DOA估计

提出了两种以欠采样率实现盲谱感知与二维波达方向(DOA)估计的算法。这两种算法利用了MWC的周期信号所对应的傅里叶级数系数矩阵来确定未知信号频谱所在的子频带位置,然后利用二维阵列的信号模型估计得到包含二维DOA的空间相位。通过空间相位信息与子频带位置信息能重构出子带谱信息,以此能得到频率的高精度估计,最后通过空间相位分别估计得到信号的方位角与俯仰角。仿真验证了所提欠采样接收机与相应算法的有效性,以及在低信噪比环境下依然有较好的鲁棒性。

认知无线电频谱感知技术在频谱管理中的应用

本文通过对认知无线电频谱技术的简述,介绍了认知无线电频谱技术的基本概念和思想,提供了认知无线电频谱技术基本的监测方法,强调了认知无线电频谱技术多天线与协同监测的要点,并结合无线电的基本工作和近期发展经验,提供了认知无线电频谱感知技术在频谱管理中的应用要点,希望能够在做好无线电监测工作的基础上,使认知无线电频谱感知技术发挥出更加科学、社会和经济价值,突出认知无线电频谱感知技术的综合价值。

谱压缩感知的非凸低秩矩阵优化模型与方法综述

谱压缩感知研究有限样本下谱稀疏信号及其连续频率参数的恢复问题,是经典的信号频谱分析问题的拓展,在阵列与雷达信号处理和无线通信等信息技术领域中有着广泛应用.谱压缩感知的经典方法和本世纪诞生的凸松弛方法由于适用范围、估计精度或算法速度的制约,无法满足当下5G和未来6G无线通信等技术对于高精度、高速度的迫切需求.近期,一系列基于结构低秩矩阵的非凸优化模型被提出,这些模型通过精确刻画谱稀疏信号的几何结构,将具有严重非凸性的原参数域优化问题等价刻画为信号域中的结构低秩矩阵恢复问题,为谱压缩感知问题提供了全新的求解思路,带来了算法精度的本质提升.本文系统性地综述了已有关于单通道、多通道和恒模这三类常见谱稀疏信号的结构低秩矩阵恢复模型和求解算法,分析了这些模型的共同点和差异性,并对未来可能的研究方向进行了展望.

基于空间信道控制方式的频谱感知资源分配技术

针对频谱资源中授权频段因信道质量和信号干扰等因素导致的感知频谱利用率不高的问题,提出了基于大规模天线空间信道控制的频谱感知资源分配方式.通过波束成型技术使主瓣波束对准期望用户,而在干扰源方向放置零陷,可有效地抑制对次级用户的干扰,并确保次级用户对感知频谱的使用,从而进一步提高系统的频谱效率.仿真结果显示,在信噪比为5 dB和10 dB时,所提方案的谱效率(系统吞吐量)优于其他策略.

多频声频率成分与感知倒谱距离的初步研究

通过理论计算和主观实验,研究了多频声频率成分在不同频响曲线下与感知倒谱距离和主观评价之间的关系。针对所选定的9种频响曲线和7种节目信号,当频率范围为1～10kHz之间时,感知倒谱距离与主观评价的相关系数最高仅为0.93,最低为0.80,7种信号中只有3种的主客观相关系数高于0.90。而当频率范围为50～15000Hz时,感知倒谱距离与主观评价的相关系数最大达到了0.96,最低为0.85,7种信号中有5种的主客观相关系数都高于0.90。从而得出了在频率成分个数较少的情况下,频率范围比频率间隔需要更优先考虑的结论。

多频声感知倒谱距离与音质评价的研究

文中以多频声作为测试信号,研究电声系统中感知倒谱距离与音质评价的关系。主观评价实验表明,采用多频声信号和听觉感知模型得出的感知倒谱距离能很好地反映频响曲线对音质的影响。当选取合适的评价节目源时,主客观评价结果的相关度均可达到0.84以上,最高可达到0.96。

先进感知技术在电池状态估计中的应用与启示

实时精准的电池状态估计对锂离子电池高效安全运行尤为重要。以先进感知技术对电池内部特征进行原位在线感知,可为电池应用提供丰富的数据支撑,是电池状态估计方法发展的关键。以基于电气测量特征的、基于模型的和基于数据驱动与机器学习的电池状态估计方法作为对比,辨析基于光纤感知技术、电化学阻抗谱感知技术、机械应变感知技术、声学感知技术的先进智能感知技术原理、应用和优势缺陷,构建未来智能电池与智能电池管理系统。

短时谱特征的汉语重音检测方法研究

重音是语言交流中不可或缺的部分,在语言交流中扮演着非常重要的角色。为了验证基于听觉模型的短时谱特征集在汉语重音检测方法中的应用效果,使用MFCC(Mel frequency cepstrum coefficient)和RASTAPLP(relative spectra perceptual linear prediction)算法提取每个语音段的短时谱信息,分别构建了基于MFCC算法的短时谱特征集和基于RASTA-PLP算法的短时谱特征集;选用NaiveBayes分类器对这两类特征集进行建模,把具有最大后验概率的类作为该对象所属的类,这种分类方法充分利用了当前语音段的相关语音特性;基于MFCC的短时谱特征集和基于RASTA-PLP的短时谱特征集在ASCCD(annotated speech corpus of Chinese discourse)上能够分别得到82.1%和80.8%的汉语重音检测正确率。实验结果证明,基于MFCC的短时谱特征和基于RASTA-PLP的短时谱特征能用于汉语重音检测研究。

Hot spot tracking of flood remote sensing research over the past 22 years:abibliometric analysis using CiteSpace

Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.

基于改进感知非均匀谱压缩的鲁棒语音识别算法

针对语音识别系统在噪声环境下识别率下降的问题,提出改进的感知非均匀谱压缩特征提取算法,它能在噪声环境下对语音信号进行有效的压缩,并且使训练与识别环境更加匹配,最终达到提高识别率的目的.在智能轮椅平台上进行语音识别验证,结果表明,该算法能有效地提高语音识别系统的鲁棒性,保证语音识别系统在噪声环境下的识别率.

基于atan-LMS与PLPC相结合的稳健语音识别

将一种改进的变步长算法(atan-LMS)应用于自适应滤波器,并将之与感知线性预测倒谱技术(PLPC)相结合,简记为atan-LMS+PLPC。该方法通过优化步长和误差之间的非线性关系,使降噪效果更加显著,然后对增强语音提取感知线性预测倒谱系数(PLPC)作为特征参数。试验结果表明,基于atan-LMS+PLPC的系统其识别性能要比基于Sigmind函数(SVSLMS+PLPC)优越,并且信噪比越低识别率提高幅度越大。

Optimal decision threshold for soft decision cooperative spectrum sensing

In order to achieve higher spectrum efficiency in cognitive radio （CR） systems, a closed-form expression of the optimal decision threshold for soft decision cooperative spectrum sensing based on the minimum total error probability criterion is derived. With the analytical expression of the optimal decision threshold, the impact of different sensing parameters on the threshold value is studied. Theoretical analyses show that the optimal threshold achieves an efficient trade-off between the missed detection probability and the false alarm probability. Simulation results illustrate that the average signal-to-noise ratio （SNR） and the soft combination schemes have a great influence on the optimal threshold value, whereas the number of samples has a weak impact on the optimal threshold value. Furthermore, for the maximal ratio combing （MRC） and the modified deflection coefficient （MDC） schemes, the optimal decision threshold value increases and approaches a corresponding individual limit value while the number of CR users increases. But the number of CR users has a weak influence on the optimal decision threshold for the equal gain combining （EGC） scheme.

一种新的非线性失真评价方法

提出了一种新的非线性失真客观评价方法。评价信号选用多频声,原始信号和失真后的信号通过听觉模型处理,得到感知倒谱距离,在此基础上将感知倒谱距离计权后得到所需要的客观评价参量。主观验证实验表明,当选取合适的评价节目源时,主客观评价结果的相关度均可达到0.84以上。

Cooperative Spectrum Sensing Exploiting a Certain Number of CR Users Based on Multi- Antennas in Cognitive Radio Networks

In this paper,a cooperative spectrum sensing scheme,which is based on cooperation of a certain number of secondary users and cooperative diversity under multi-antenna scenario,is proposed.Under multi-antenna scenario,we set a targeted detection probability and optimize the false alarm probability of the network by choosing a certain number of secondary users with the highest primary user’s signal to noise ratio.The detection performance of the network is also evaluated when all the secondary users are cooperating to illustrate the benefits of the proposed scheme as a contrast.In addition,how to choose the detection threshold of the secondary user is analyzed for the purpose of decreasing the average risk.Theory analysis and simulation results show that the optimum false alarm probability can be derived by cooperating a certain number of secondary users rather than all the secondary users and the detection performance of the network can be further improved if secondary users are equipped with multiple antennas.Also,a minimum average risk can be obtained by optimizing the detection threshold.

A novel weighed cooperative bandwidth spectrum sensing for spectrum occupancy of cognitive radio network

In cognitive radio network(CRN), a secondary user(SU) may utilize the spectrum resource of the primary user(PU) and avoid causing harmful interference to the primary network(PN) via spectrum sensing. In the traditional time spectrum sensing, the SU cannot detect the PU's presence during its transmission, thus increasing interference to the PN. In this work, a novel weighed cooperative bandwidth spectrum sensing method is proposed, which allows multiple SUs to use part of the bandwidth to perform cooperative spectrum sensing throughout the whole frame in order to detect the PU's reappearance in time. The SU's spectrum efficiency is maximized by jointly optimizing sensing bandwidth proportion, number of cooperative SUs and detection probability, subject to the constraints on the SU's interference and the false alarm probability. Simulation results show significant decrease on the interference and improvement on the spectrum efficiency using the proposed weighed cooperative bandwidth spectrum sensing method.

Matched Filter Based Spectrum Sensing When Primary User Has Multiple Power Levels

In this paper,we investigate the matched filter based spectrum sensing in a more reasonable cognitive radio(CR) scenario when the primary user(PU) has more than one transmit power levels,as regulated in most standards,i.e.,IEEE 802.11 Series,GSM,LTE,LTE-A,etc.This new multiple primary transmit power(MPTP) scenario is specialized by two different targets:detecting the presence of PU and identifying the power level.Compared to the traditional binary sensing where only the presence of PU is checked,SU may attain more information about the primary network(making CR more "intelligent") and design the subsequent optimization strategy.The key technology is the multiple hypothesis testing as opposed to the traditional binary hypothesis testing.We discuss two situations under whether the channel phase is known or not,and we derive the closed form solutions for decision regions and several performance metrics,from which some interesting phenomenons are observed and the related discussions are presented.Numerical examples are provided to corroborate the proposed studies.

Detection Performance Analysis of Spectrum Sensing in Cognitive Radio Networks with Mobile Secondary Users

The majority of existing papers about spectrum sensing have the assumption that secondary users(SUs) are stationary. However,mobility is an essential feature of mobile communications networks. In this paper,the detection performance of spectrum sensing by mobile SUs was analyzed. Three performance metrics,i.e.,detection probability,miss detection probability and false alarm probability,were thoroughly investigated. In our analysis,a critical variable was the real-time received primary user signal power by a mobile SU. Its probability distribution and mathematical expectation were analytically derived. Moreover,the three performance metrics in single-node spectrum sensing and multi-node collaborative spectrum sensing systems were also derived. Extensive simulations were performed. The results are consistent with the theoretical analysis. And it is concluded that SU mobility has a significant impact on the detection probability and the miss detection probability,but not on the false alarm probability.

A K-means clustering based blind multiband spectrum sensing algorithm for cognitive radio

In this paper,a blind multiband spectrum sensing(BMSS)method requiring no knowledge of noise power,primary signal and wireless channel is proposed based on the K-means clustering(KMC).In this approach,the KMC algorithm is used to identify the occupied subband set(OSS)and the idle subband set(ISS),and then the location and number information of the occupied channels are obtained according to the elements in the OSS.Compared with the classical BMSS methods based on the information theoretic criteria(ITC),the new method shows more excellent performance especially in the low signal-to-noise ratio(SNR)and the small sampling number scenarios,and more robust detection performance in noise uncertainty or unequal noise variance applications.Meanwhile,the new method performs more stablely than the ITC-based methods when the occupied subband number increases or the primary signals suffer multi-path fading.Simulation result verifies the effectiveness of the proposed method.