一种OFDM系统信道密钥生成方法

提出了一种在正交频分复用(OFDM)系统中提取信道频率响应(CFR)表征信道特征,经过探测、量化、协商、一致性确认以及隐私放大生成信道密钥的方法。首先,利用OFDM系统多载波的优势,在各个子信道提取信道特征,并进行量化;然后,采用滑窗密钥生成机制以及分段校验,通过一次位置信息交互,高效地完成协商,生成初始信道密钥;最后,利用隐私放大函数生成信道密钥。所提方法通过滑窗生成大量密钥比特,以一轮协商代替多轮协商;只限定初始密钥最大长度,使得初始密钥在长度及信息两个维度保持随机化。     

An Efficient Representation Method for ICLD with Robustness to Spectral Distortion

The inter‐channel level difference (ICLD) is a cue parameter to estimate spectral information in binaural cue coding that has been recently in the spotlight as a multichannel audio signal compression technique. Even though the ICLD is an essential parameter, it is generally distorted by quantization. In this paper, a new modified ICLD representation method to minimize the quantization distortion is proposed by adopting a flexible determination of the reference channel and the unidirectional quantization scheme. Our experimental result confirms that the proposed method improves the multichannel audio output quality even with the reduced bit‐rate.     

基于视觉感知的高效视频编码标准帧内量化矩阵优化方法

该文使用一种基于离散余弦变换(DCT)的调制传递函数(MTF)改进高效视频编码标准(HEVC)帧内量化矩阵,并在计算过程中采用一种新的空间频率计算方法。HEVC整数DCT变换是在DCT变换的基础上经过伸缩扩展以及微调后得到的,与DCT变换有所区别,因此针对这种差别进一步优化校正量化矩阵。仿真过程中设计一种基于结构相似性(SSIM)的Bjontegaard比特率差值(BDBR)方法来测试量化矩阵的编码性能,结果表明在相同的视频质量下,该文设计的量化矩阵能进一步降低码率。该方法在编码过程中只需要简单地改变量化矩阵,没有影响编码模型的算法结构,也不会增加编码的复杂度。     

Toward intelligent resource management in dynamic Fog Computing-based Internet of Things environment with Deep Reinforcement Learning: A survey

Fog computing has already started to gain a lot of momentum in the industry for its ability to turn scattered computing resources into a large-scale, virtualized, and elastic computing environment. Resource management (RM) is one of the key challenges in fog computing which is also related to the success of fog computing. Deep learning has been applied to the fog computing field for some time, and it is widely used in large-scale network RM. Reinforcement learning (RL) is a type of machine learning algorithms, and it can be used to learn and make decisions based on reward signals that are obtained from interactions with the environment. We examine current research in this area, comparing RL and deep reinforcement learning (DRL) approaches with traditional algorithmic methods such as graph theory, heuristics, and greedy for managing resources in fog computing environments (published between 2013 and 2022) illustrating how RL and DRL algorithms can be more effective than conventional techniques. Various algorithms based on DRL has been shown to be applicable to RM problem and proved that it has a lot of potential in fog computing. A new microservice model based on the DRL framework is proposed to achieve the goal of efficient fog computing RM. The positive impact of this work is that it can successfully provide a resource manager to efficiently schedule resources and maximize the overall performance.     

相位量化DRFM性能分析

本文提出了一种数字射频存储器(DRFM)偏移相位量化方法,它是在传统相位量化方法的基础上通过增加一个相位偏移而得到.通过傅立叶分析,得到了其谐波寄生信号性能计算公式,结果表明,其性能与传统相位量化方法相同.其优点是,由于其良好的对称性,更适合于硬件电路实现.在此基础上,提出了一种用D/A变换器代替加权求和网络实现相位量化DRFM信号重构的新方法,解决了多位相位量化DRFM工程实现的困难.采用计算机仿真方法对由于D/A变换幅度量化引起的性能变化进行了分析,表明当D/A变换器位数接近于相位量化位数时就可以获得满意的效果.     

一种改进的模糊C-均值聚类算法在说话人识别中的应用

提出了一种将改进的FCM聚类算法与矢量量化相结合的说话人识别的方法。先从语音信号中提取待识别的特征矢量集,再利用矢量量化来设计码本,最后用改进的算法对待识别语音进行辩识。该算法解决了FCM算法对初始值敏感、易陷入局部最优的问题。所使用的特征参数较少,计算比较简单,但识别率较高,且具有较好的鲁棒性。     

Practical robust uniform channel decomposition scheme for CSI mismatch in limited feedback MIMO systems

Uniform channel decomposition (UCD) has been proven to be optimal in bit error rate (BER) performance and strictly capacity lossless when perfect channel state information (CSI) is assumed to be available at both the transmitter and receiver side. In practice, CSI can be obtained by channel estimation at receiver and conveyed to transmitter via a limited-rate feedback channel. In such case, the implementation of traditional UCD by treating the imperfect CSI as perfect CSI cause significant performance degradation due to inevitable channel estimation error and vector quantization error. To overcome this problem, a practical robust UCD scheme was proposed in this paper, which includes two steps, firstly, a matching architecture was proposed to eliminate the mismatch between CSI at receiver (CSIR) and CSI at transmitter (CSIT), secondly, an MMSE based robust UCD scheme considering channel estimation error and vector quantization error as an integral part of the design was derived. Simulation results show that the proposed practical robust UCD scheme is capable of improving the BER performance greatly in the context of channel estimation error and vector quantization error compared with the traditional UCD scheme.     

时间相关信道的量化反馈算法研究

针对实际通信信道的时间相关特性,提出了一种基于有限状态向量量化（FSVQ）的信道量化算法。算法首先将信道划分为有限个状态,并为每一状态设计一个码本,码本用来量化从对应状态转移而来的信道。接收机只需要反馈信道向量在状态码本中的量化码字的序号。跟不考虑时间相关的向量量化算法相比,该算法能以相同的反馈负荷获得更高的性能。仿真结果表明,当信道相关系数为0.9,发射天线为6个,发射信噪比为0 dB的时候,该算法能提高接收信噪比性能1 dB。     

Finite-State Markov Wireless Channel Modeling for Railway Tunnel Environments

In recent years,high-speed railways(HSRs)have developed rapidly with a high transportation capacity and high comfort level.A tunnel is a complex high-speed rail terrain environment.It is very important to establish an accurate channel propagation model for a railway tunnel environment to improve the safety of HSR operation.In this paper,a method for finite-state Markov chain(FSMC)channel modeling with least squares fitting based on non-uniform interval division is proposed.First,a path loss model is obtained according to measured data.The communication distance between the transmitter and receiver in the tunnel is non-uniformly divided into several large non-overlapping intervals based on the path loss model.Then,the Lloyd-Max quantization method is used to determine the threshold of the signal-to-noise ratio(SNR)and the channel state quantization value and obtain the FSMC state transition probability matrix.Simulation experiments show that the proposed wireless channel model has a low mean square error(MSE)and can accurately predict the received signal power in a railway tunnel environment.     

基于DCT-TCQ的SAR原始数据压缩算法

该文提出了一种基于离散余弦变换(DCT)和网格编码量化(TCQ)的SAR原始数据压缩算法。SAR原始数据可以看成是距离向和方位向的2维线性调频信号的线性平移叠加,因而含有丰富的频率分量,对DCT系数按频率分组,根据各组频率分量系数方差确定量化比特分配,然后对量化系数进行TCQ编码量化,可以大幅度提高了量化增益。真实SAR原始数据实验结果表明:该算法使SAR原始数据的压缩性能指标较现有算法有了明显提高。     

基于多级零树编码的小波系数网格编码量化

该文提出了对小波图像作多级零树编码后进行网格编码量化（TCQ）的新方法。首先利用子带间的相关性进行零树编码，然后利用卷积编码和信号空间扩展来增大量化信号间的欧氏距离，并用Viterbi算法寻找最优量化序列。仿真结果表明，该方法比零树编码后采用最优量化要提高0．3dB左右。该方法还具有编码计算复杂度适中，解码简单的优点。     

基于USB的DMA方式实现CCD高速数据采集

提出一种CCD用于通用串行总线(USB)高速数据采集系统设计的新方法。通过对USB及其DMA传输方式分析,采用无外部缓冲区的结构实现数据的高速传输．具有精度高、结构简单、方便易行等优点。详细描述了硬件、固件、驱动程序及应用程序的设计情况。实验证明,该系统可实现CCD的高速高精度数值量化及数据传输。     

波形内插语音编码器中快渐变波形幅度的量化

在低速率参数语音编码算法中,如何用有限的比特数有效地量化幅度谱是一个关键问题。本文对波形内插语音编码模型中快渐变波形幅度的量化问题进行了深入研究和分析,提出了一种基于矢量变维和DCT的REW幅度感觉加权量化方案,该方法降低了编码比特数,减少了存储和计算复杂度,增强了编码语音的感性质量。主观听力测试结果表明该量化方案在每帧4比特时的WI语音编解码质量要优于用基于DCT的REW幅度矩阵量化方案在每帧10比特时的重建语音质量。     

OFDM系统基于矩阵开方的盲信道估计

该文提出一种基于矩阵开方(computing Roots of Matrices, RM)的盲信道估计算法和一种自适应矩阵开方(Adaptive computing Roots of Matrices, ARM)盲信道估计算法。RM算法利用信息符号的有限字符集特性,在时域上通过对一个Toeplitz下三角矩阵开方进行信道解卷积,得到信道估计的闭合解。该运算复杂度远低于现有的盲信道估计搜索算法,并且适用于信道阶数较大、搜索算法不能处理的情况。仿真结果表明RM信道估计性能接近于搜索算法的最佳性能,而ARM通过最陡下降迭代将代价函数最小化,可以进一步提高信道估计的准确性。     

遥感图像自适应分层量化的快速DCT压缩法

依据遥感图像的频谱特性,提出一种自适应分层量化的快速DCT图像压缩算法,在对原始图像快速DCT之后,根据图像频谱特性自适应修正JPEG量化表,再用新量化表分层量化DCT系数。真实遥感图像压缩实验表明,在同等压缩比下,提出的方法比标准JPEG方法速度快,且峰值信噪比增加1～2dB,并能实现嵌入式码流图像压缩。     

基于HMM的说话人辨认系统及其改进

对基于隐马尔可夫模型(HMM)的说话人辨认系统进行了讨论,完成了系统设计。对系统中矢量量化这一关键性环节进行了改进,提出了一种新的基于遗传算法的码本生成方法。测试结果表明,改进后的系统具有较高的正确识别率,特别是在与文本无关的情况下。     

基于JPEG序列的图像重建

该文提出了一种基于JPEG序列的图像重建方法。该方法在已有的单帧图像复原技术的基础之上,依据超分辨率重建的思想,将凸集投影(POCS)理论与迭代反投影(IBP)算法相结合,在频域内降低量化误差,修复离散余弦系数。此外,它还利用了最大后验概率(MAP)估计以及相应优化算法的特点,在去除高斯噪声的同时,保护边缘和细节信息。实验结果表明,该方法一方面能够抑制高比率压缩所造成的块效应和振铃效应,另一方面能较好地恢复图像的细节部分,有效地提高图像的清晰度。     

New high-performance reversible data hiding method for VQ indices based on improved locally adaptive coding scheme

In this paper, a new high-performance reversible data hiding method for vector quantization (VQ) indices is proposed. The codebook is firstly sorted using the unidirectional static distance-order technique to improve the correlation among the neighboring indices. The two-dimensional structure of image and the high correlation among the neighboring blocks are used to update the self-organized list L in the improved locally adaptive coding scheme (ILAS). Then a new embedding rule according to the complexity of the region at which the current block locates and the position of current block index in the list L is proposed to obtain a better embedding capacity. The experimental results demonstrate that our proposed method has a better performance in terms of compression rate, embedding capacity and embedding rate compared with the related data hiding methods.     

一种基于系统采样时钟量化的无人机测距方法

为了解决基于单载波频域均衡(SC-FDE)高速数据链无人机测控系统的地空双向距离测量问题,提出了一种基于系统采样时钟量化的无人机测距方法,将机载/地面测距信息分别量化至机/地系统采样时钟的计时器,完成地空双向距离的测量。理论分析和地空链路测试平台实验结果表明,新方法在复杂多径环境下实现了地空双向距离测量,降低了测距分系统的设计复杂度,地空双向实际测距均值与等效自由空间传输延迟一致,且测距精度满足理论测距误差设计值。该测距方法在无人机测控系统中具有良好的应用前景。