基于FrFT限波的OFDM系统LFM干扰抑制方法

线性调频（LinearFrequencyModulation,LFM）干扰是一种典型的非平稳宽带干扰信号。分析了线性调频干扰对正交频分复用（OrthogonalFrequencyDivisionMultiplexing,OFDM）系统性能的影响;为了降低LFM干扰对OFDM性能的影响,利用OFDM信号和LFM干扰在分数傅立叶变换（FractionalFourierTransform,FrVr）域分布特征的差异,提出基于FrFr限波的OFDM系统LFM干扰抑制方法。理论分析和仿真结果表明：该方法具有较好的LFM干扰抑制能力。     

基于分数阶Fourier变换的局部放电去噪方法

为了提取局部放电信号的特征,提出了一种基于分数阶Fourier变换(FRFT)的局部放电噪声抑制方法。首先对含有白噪声的局部放电信号做分数阶Fourier变换,在分数阶Fourier变换域内,随着阶次p的变化,信号能量谱呈现不同的时频聚集性,利用最小均方误差准则扫描到最优分数阶Fourier变换域,然后根据法则利用小波分解算法得到的噪声强度在最优分数阶Fourier变换域内对含噪信号进行去噪处理,从而抑制噪声干扰。相比传统小波去噪算法,该方法具有自适应性强,且处理效果明显等优点。分别对仿真信号和实际信号进行分析,验证了本文方法的有效性与可行性。     

一种可应用于OFDM水声通信系统中的同步算法

本文在阐述和构造了GMW序列的基础上,提出一种适用于OFDM水声通信系统的联合同步方法.此方法根据经典同步算法Schmidl和Minn的符号定时原理,传输并处理文中所构造的GMW序列信号,最终利用GMW序列的相关性和相位关系完成联合同步判决.计算机仿真结果表明,在水声信道下此方法可以很好完成时间同步和载波频率同步.     

基于FDRM的降低频率偏差对OFDM系统影响的新方法

OFDM技术具有很高的频带利用率,被认为是第四代移动通信的核心技术.但是,OFDM系统对于频率偏差较为敏感.该文研究了从降低频率偏差对OFDM系统影响的角度出发,研究了基于FDRM的新方法,给出了充分的理论依据,和详细的证明过程.     

异质网间通信信道单谱脉冲响应频率估计

提出一种基于Hough变换的单谱特征分析的异质网通信信道脉冲响应频率估计算法。采用正交频率复用OFDM训练起始帧,加入多径分量的相位偏移进行相位偏转修正,构建了信道模型与脉冲响应信号模型。频率估计中采用Hough变换把信号空间中的直线的检测问题转换为参数空间中对点的检测问题,计算多普勒单谱脉冲响应信号的约束经验模态值,实现Hough变换单谱脉冲信号检测和脉冲响应频率估计算法改进。仿真实验表明,该算法能准确估计出异质网间通信脉冲响应信号的频率值,精度较传统方法高,在最小的迭代步数下实现算法收敛,误码率为0,性能较优。     

一种OFDM系统中新的定时估计算法

OFDM系统对定时误差更为敏感,对同步精度要求更高,且实现起来比较复杂,是OFDM系统实现的一个难点。基于一种OFDM系统定时估计算法的研究提出了一种新的训练符号结构,这种结构利用了时域中的对称共轭结构,该方法具有更准确的符号定时的特性,定时的均方误差要优于Park算法。     

基于训练序列的OFDM粗帧定时同步算法分析

本文详细分析了OFDM系统基于训练序列的三种定时同步算法.这三种算法采用了不同的训练序列,但都基于能量归一化的最大相关原则.利用定时错误概率对三种定时同步算法性能进行了比较.Matlab仿真结果可以看出:Schmidl & Cox定时同步算法方差较大,Park& Cheon定时同步算法最为稳定,而Minn &Letaief算法性能介于两者之间.本文的分析为OFDM符号定时同步的应用研究提供了一定的借鉴作用.     

802.11协议OFDM接收部分同步实现方案

阐述了OFDM算法在通信系统中的应用和其优点,具体描述了在接收部分OFDM同步的实现,主要介绍了OFDM系统中同步所应用到的定时同步,载波频率同步和采样同步以及剩余相位补偿算法的实现。同步技术是任何—个通信系统都需要解决的实际问题;直接关系到通信系统的整体性能。没有准确的同步算法,就不可能进行可招的数据传输,同步技术是信息可靠传输的前提。     

正交频分复用系统中同步技术研究

正交频分复用(oFDM)是一种高效的调制传输技术,将被广泛地应用于新一代无线通信系统中.文章介绍了OFDM系统的基本概念,引出OFDM系统的同步技术的重要性,重点对栽波同步、符号同步及样值同步作了原理陈述和性能分析,介绍了同步算法基本步骤,并提出了同步算法的实现方案.     

一种频域信噪比估计方法

本文介绍一种在频域对接收信号进行信噪比估计的算法,该算法不需要接收信号的先验信息,并且具有计算速度快,在频率偏差较大的情况下仍然有较好的估计效果.     

Blind estimation of carrier frequency offset for OFDM systems with time-varying DC offset

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in radar and communication systems. High sensitivity to carrier frequency offset (CFO) is one of the major drawbacks of OFDM. CFO estimation for OFDM systems had been extensively studied and various algorithms had been proposed. However, the established algorithms may be compromised by the adoption of direct-conversion architecture and multi-mode low noise amplifier in the OFDM receiver, which introduces time-varying direct current offset (TV-DCO) into the system. In our previous study, we developed an eigen-decomposition based estimation algorithm, which is robust to TV-DCO but suffers from performance degradation under low to medium signal-to-noise ratio and requires high computation efforts. To address those issues, we in this paper propose a novel blind CFO estimation algorithm. By making use of the second order differential filtering and subspace method, the proposed algorithm achieves great performance improvement with reduced complexity. The performance of the proposed algorithm is demonstrated by simulations.     

On the CFO estimation of the OFDM: A frequency domain approach

This paper presents a simple yet effective carrier-frequency-offset (CFO) estimation algorithm for orthogonal-frequency-division-multiplexing (OFDM) system using a general inter-carrier interference (ICI) self-cancellation scheme. The key feature of the proposed algorithm is that it has a significantly wider CFO estimation range than the maximum-likelihood (ML) algorithm. The proposed algorithm first constructs the snapshot vectors through stacking the signals in each subcarrier group of the precoded OFDM symbol. Because the ICI self-cancellation scheme can suppress the inter-group ICIs, the snapshot vectors can be expressed in a form having a CFO-directed response structure. This enables the proposed approach to estimate the CFO by using the multiple signal classification (MUSIC) algorithm in the frequency domain.     

Maximum likelihood carrier frequency offset estimation algorithm with adjustable frequency acquisition region

This study presents a simple yet effective carrier frequency offset (CFO) estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems. At the transmitter, the proposed algorithm uses null subcarriers to render the OFDM signal periodic in the time domain. At the receiver, these periodic time samples become CFO-bearing signals, which can be adopted to develop the maximum likelihood (ML) CFO estimation algorithm accordingly. In addition to providing reliable and efficient CFO estimation, the proposed algorithm has an adjustable acquisition region linearly proportional to the order of the null subcarrier insertion scheme.     

A polynomial-rooting algorithm for CFO estimation in slow fading OFDM

This study proposes a polynomial-rooting algorithm to estimate the carrier-frequency-offset (CFO) of a slow fading orthogonal frequency division multiplexing (OFDM) system. The proposed algorithm uses an inter-carrier-interference self-cancellation (ICI-SC) scheme to precode data symbols at the transmitter. This study derives the time coherency of the precoded OFDM signal, which enables the proposed algorithm to estimate the CFO through array signal processing techniques. The proposed algorithm first constructs snapshot vectors using the receive time samples at specific positions. This snapshot vector is CFO-directed because of the coherent property of the precoded signal. Using the second order statistics of the snapshot vector, the proposed approach estimates the CFO through a polynomial-rooting algorithm.     

MIMO-OFDM通信系统研究

采用多入多出(MIMO)技术可以提高信道容量和信道可靠性,降低误码率.正交频分复用(OFDM)是一种特殊的多载波传输方案,各子载波在整个符号周期上正交,各子载波信号子频谱可以互相重叠,提高频带利用率.MIMO-OFDM技术是OFDM与MIMO技术结合形成的一种新技术,该技术是在OFDM传输系统中采用阵列天线实现空间分集,提高了信号质量.本文全面介绍了MIMO技术和OFDM技术及两者的结合,分析实现MIMO-OFDM技术的框架.     

MIMO-OFDM技术研究

多入多出(MIMO)系统在发射端和接收端分别设置多副天线,采用MIMO技术可以提高信道容量和信道可靠性,降低误码率。正交频分复用(OFDM)是一种特殊的多载波传输方案,各子载波在整个符号周期上正交,各子载波信号子频谱可以互相重叠,提高了频带利用率。MIMO-OFDM技术是OFDM与MIMO技术结合形成的一种新技术,该技术是在OFDM传输系统中采用阵列天线实现空间分集,提高了信号质量。本文中全面介绍了MIMO技术和OFDM技术及两者的结合,分析了实现MIMO-OFDM技术的框架,未来的工作是如何用硬件来仿真实现这个系统。     

基于局域波分解的机动目标ISAR成像

在飞机一类惰性较大的目标作机动飞行时,在ISAR成像的短时间内,同一距离单元的回波可近似看成多分量线性调频信号的叠加,且调频斜率各不相同. 因此,在ISAR成像的横向压缩处理中,先利用局域波分解方法将每个距离单元的回波分解为若干个线性调频信号,然后再进行Wigner-Ville变换(WVT),获取目标的瞬时多普勒谱,进而实现目标的二维成像. 该处理方法不仅避免了WVT交叉干扰项的产生,而且在保持WVT的高时频分辨能力的同时减少了计算量,仿真结果证明了该方法的有效性.     

降低OFDM系统中峰平比方法研究

OFDM系统的一个主要缺点是峰均功率比过高。部分传输序列(PTS)的方法可以改善OFDM符号的峰均功率比的性能,它是一种很有效的方法。本文在传统的PTS方法基础上进行改进,提出了一种减小复杂度的方法。     

Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a genetic algorithm support vector machine (GA-SVM) based symbol detection method is proposed for chaotic baseband wireless communication system (CBWCS), by this way, treating the problem from a different viewpoint, the symbol decoding process is converted to be a binary classification through GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at the receiver, so as to improve the bit error rate (BER) performance of the CBWCS and simplify the symbol detection process by removing the channel identification and the threshold calculation process as compared to that using the calculated threshold to decode symbol in the traditional methods. The simulation results show that the proposed method has better BER performance in both the static and time-varying wireless channels. The experimental results, based on the wireless open-access research platform, indicate that the BER of the proposed GA-SVM based symbol detection approach is superior to the other counterparts under a practical wireless multipath channel.