水声通信系统中双向turbo均衡算法

提出了一种水声通信系统中直接自适应双向turbo均衡算法。摒弃了信道估计步骤,采用基于直接自适应的turbo均衡器,并利用内嵌数字锁相环的判决反馈均衡器结构跟踪时变信道,采用最速优化算法自适应调整迭代步长,使得收敛速度和算法性能得到很好折中。此外,利用最小均方误差准则,得到最优权重因子,对正向与反向turbo均衡结果加权求和,消除误差传播效应。仿真和湖上实验验证了方法的正确性,双向均衡的性能优于单向均衡。湖上实验结果表明,基于直接自适应算法相比于基于信道估计的算法,对时变信道不敏感,能获得更低的误比特率。      

单载波频域均衡中的水声信道频域响应与噪声估计

本文对单载波频域均衡(SC-FDE)中的信道和噪声估计进行研究,理论分析了形成误码率平台的可能原因,提出了基于Chu序列的联合信道时频域响应和噪声功率估计算法.利用水声信道响应的稀疏特性和门限确定信道能量集中区域,进行信道估计的去噪处理和噪声功率计算.借助于水声射线模型对所提出的算法进行了仿真,分析了噪声估计对SC-FDE性能的重要影响.仿真结果表明,所提出的信道和噪声估计算法可以有效减缓或消除SC-FDE中的误码率平台.     

稀疏信道条件下正交多载波扩频水声通信无导频信道估计算法

提出了一种无导频信道估计算法,该算法首先利用水声信道的稀疏特性对扩频信号进行识别,再利用扩频信号识别结果对水声信道响应进行判决反馈估计,从而实现对稀疏水声信道响应的可靠重建。仿真和湖上试验结果证实:无导频信道估计算法可以在不降低通信质量的前提下,实现对水声通信信号的稳健均衡,提升水声通信速率。      

块稀疏水声信道的改进压缩感知估计

压缩感知信道估计可利用信道稀疏特性提高估计性能,但对于具有典型块稀疏分布的水声信道,经典的l₀或l₁范数无法很好地描述块稀疏特性。利用水声信道块稀疏分布规律特性提出一种能够识别块稀疏结构的块稀疏似零范数,并在稀疏恢复信道估计算法中引入块稀疏似零范数约束项,进一步推导了复数域块稀疏似零范数恢复迭代算法,该算法通过对块稀疏似零范数进行梯度下降迭代并将梯度解投影至解空间来获得水声信道的块稀疏似零范数估计。数值仿真和海上水声通信实验结果表明该算法相对经典的稀疏信道估计算法有较明显的性能改善。通过算法推导、仿真和实验可获取结论:利用水声信道的块稀疏特性进行压缩感知重构可有效提高信道估计性能。      

用于单载波频域均衡水声通信的可分近似稀疏信道估计

针对单载波频域均衡水声通信中信道估计易受噪声干扰的问题,提出了一种低复杂度的信道估计方法。考虑水声信道的时域稀疏特性,导出频域输入、输出信号与信道冲激响应的关系式,并引入稀疏正则项,构造稀疏信道估计的目标函数。然后利用可分近似对目标函数进行迭代优化,再经过稀疏化与去偏处理,得到信道传递函数的最终估计。最后,利用数值仿真和海试数据对所提出方法的性能和运算效率进行评估。较之传统信道估计方法,所提出的方法在估计精度和计算复杂度方面具有一定的优势。      

基于改进基追踪去噪的水声单载波频域均衡信道估计

针对低信噪比下单载波频域均衡信道估计精度低导致均衡效果差的问题,利用基追踪去噪方法估计稀疏信道有效抽头具有的稳定特性,提出了一种在平稳水声信道条件下单载波频域均衡系统中应用的改进基追踪去噪稀疏信道估计方法。方法利用延拓循环前缀作为导频进行多次信道估计,将估计结果累积平均,再采用门限判决提取有效抽头得到信道估计值,从而提高了稀疏信道的估计精度。通过合理选择循环前缀延拓次数,尽可能地降低导频长度对频带利用率的影响。仿真对比分析了改进基追踪去噪算法与匹配滤波、最小平方以及传统基追踪去噪信道估计算法的性能以及对单载波频域均衡系统误码率的影响。仿真实验结果表明,改进基追踪去噪信道估计算法具有更优的估计精度,在单载波频域均衡系统中可以提高约3 dB的接收信噪比增益。     

基于LMS/SOLMS算法的时变多径水声信道估计方法

缓变信道下基于LMS算法的信道估计算法具有较好的跟踪性能,但对快变信道,LMS算法跟踪性能下降。SOLMS算法具有比LMS算法更好的跟踪性能,尤其是在快变的信道下。但由于SOLMS算法在收敛阶段的振荡性,这时收敛速度较LMS算法慢。本文提出一种收敛模式下用LMS算法获得信道的参数,收敛后则切换成SOLMS算法跟踪信道的变化的信道估计方法。新方法结合了LMS算法收敛快和SOLMS算法跟踪性能好的优点。对时变多径水声信道估计的计算机仿真实验证明了该方法的有效性。     

相干水声通信幅相加权空间分集均衡算法

水声信道的典型特点为多普勒频移严重、可利用带宽窄以及强多径干扰。空间分集均衡技术是相干水声通信中克服信道多径干扰,消除码间干扰的一种有效手段。为了极大化地输出阵增益,结合无源相位共轭方法和多通道均衡算法,本文设计了组合信噪干比的全新信道评价方法。利用改进的Sigmoid函数对各通道接收信号的幅度进行加权处理;采用二阶锁相环跟踪各通道信号的相位变化,实现各通道信号同相累加。将各通道低通滤波后的信号能量归一化,采用了分数阶-判决反馈分集均衡器,加入各通道权重系数实现了水声通信系统的分集均衡接收。仿真结果和湖试数据处理结果均表明,优化的幅相加权分集均衡接收算法能抵消多径和噪声的干扰,性能优于等增益合并接收算法。湖试数据处理结果误码率降低了1.8%。      

一种快速收敛的符号回归常数模盲均衡算法

为了加快符号回归常数模算法(SRCMA)的收敛速度,本文提出了一种适合于BPSK信号 的快速算法。该算法用新的误差函数替代SRCMA中的误差函数,新误差函数针对BPSK信号而 设计。新算法采用分数间隔均衡模型,有效地加快了SRCMA算法的收敛速度。水声信道仿真实验 表明,该算法性能稳定,收敛速度快,适用于快速变化信道的盲均衡和水声通信数据的实时恢复。     

混沌通信系统中非线性信道的自适应神经Legendre正交多项式均衡

针对混沌通信系统中非线性信道干扰问题，基于混沌信号重构理论和Legendre正交多项式结构，提出了一种自适应神经Legendre正交多项式信道均衡器，并给出相应的归一化最小均方算法. 仿真研究表明：所提出的自适应神经Legendre正交多项式信道均衡器能有效地消除线性和非线性信道干扰，均衡器输出信号能反映出混沌信号的特性，具有良好的抗干扰性能.该均衡器的结构简单，权系数参数较少，收敛稳定性较好. 关键词： Legendre 正交多项式 信道均衡 混沌吸引子 神经网络     

时变带限信道中光通信的均衡与去噪技术

为了解决时变带限信道中的光通信问题,基于自适应均衡和盲均衡方法,提出了一种半盲光信号均衡方案,适用于大气海洋等时变带限光信道的光通信信号处理.对带限光信道中传输的包含泊松噪音开关键控调制光脉冲信号的不同数字均衡处理方法进行了数值模拟,比较了不同方法的结果,证明半盲均衡能更快匹配光信道且能保持收敛.同时,利用小波方法研究了含噪光接收信号去噪问题,发现光滑小波软阈值去噪方法更适合空间光脉冲信号波形,并对通信眼图进行了去噪的数值模拟.     

Exploiting joint sparsity for underwater acoustic MIMO communications

MIMO communication has been recognized as a potential solution for high speed underwater acoustic communication, which unfortunately encounters significant difficulties posed by simultaneous presence of multipath and Co-channel interference (CoI). Sparsity contained in the multipath structure of underwater acoustic channels offers an effective way for improving channel estimation quality and thus enhancing the communication performance in the form of time reversal or channel estimation based equalization. However, for MIMO channels with extensive multipath and CoI, the performance gain achieved by classic sparsity exploitation channel estimation methods such as orthogonal matching pursuit (OMP) is still not enough to yield satisfactory performance. Under quasi-stationary assumption, underwater acoustic channels of adjacent data blocks exhibit correlated multipath structure, namely, multipath arrivals with similar time delay but different magnitude, which has not been exploited. In this paper, a joint sparse recovery approach is proposed to exploit the sparse correlation among adjacent data blocks to improve the performance of channel estimation. Under the framework of distributed compressed sensing (DCS), a joint sparse model which treats the multipath arrivals as sparse solutions with common time support is adopted to derive a joint sparse recovery algorithm for efficient channel estimation, the results of which are used to initialize and periodly update a channel estimation based time reversal receiver. Finally, underwater MIMO communication experimental results obtained in a shallow water channel are provided to demonstrate the effectiveness of the proposed method, compared to the same type of receiver that do not exploit the joint sparse.     

Robust modeling of acoustic paths using a sparse adaptive algorithm

Acoustic impulse response functions are generally sparse in nature and traditionally these are modeled by adaptive finite impulse response (FIR) filters trained using a least mean square (LMS) algorithm. The conventional LMS algorithm is not effective in modeling sparse systems and sparse LMS algorithms have been recently developed to improve the modeling in such scenarios. However, the traditional sparse LMS algorithms are not robust to disturbances at the error sensor and may diverge in some scenarios. With an objective to overcome this limitation of conventional sparse adaptive algorithm, this paper presents a robust sparse adaptive algorithm. The new algorithm has been shown to effectively model sparse systems in a robust manner. In addition, the algorithm has been successfully applied in modeling the acoustic feedback path in a behind the ear digital hearing aid.     

Doubly selective channel estimation and equalization based on ICI/ISI mitigation for OQAM-FBMC systems

Over a doubly selective channel, broadband transmission systems face challenges in channel estimation and equalization. High mobility causes inter-carrier interference (ICI), while multipath transmission induces inter-symbol interference (ISI). In this paper, we present a mitigation method of ICI/ISI for the offset quadrature amplitude-modulated filter bank multi-carrier (OQAM-FBMC) system. It features low inherent imaginary interference (IMI) sensitivity and high efficiency. Specifically, a pilot indices optimization algorithm and a sparse adaptive orthogonal subspace pursuit (SAOSP) algorithm are presented based on the 2-D channel modeling scheme. The guard pilots are first added to mitigate the effect of ICI. Then the index optimization and SAOSP algorithms are applied to achieve a high-accuracy estimation of sparse channel coefficients. In addition, a threshold judgment suboptimal minimum mean square error (MMSE) equalization method is presented based on the variability of the interference power. The method uses normalized interference power thresholds to estimate the ISI dimension and reduce the equalization data, thus mitigating the effect of ISI and achieving efficient equalization. To verify the above methods, single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) models are built. Simulation results indicate a 3-5 dB improvement in channel estimation accuracy. The suboptimal MMSE equalization results are close to the optimal MMSE with about four orders of magnitude reduction in complexity.     

Multi-input multi-output underwater communications over sparse and frequency modulated acoustic channels

This paper addresses multi-input multi-output (MIMO) communications over sparse acoustic channels suffering from frequency modulations. An extension of the recently introduced SLIM algorithm, which stands for sparse learning via iterative minimization, is presented to estimate the sparse and frequency modulated acoustic channels. The extended algorithm is referred to as generalization of SLIM (GoSLIM). The sparseness is exploited through a hierarchical Bayesian model, and because GoSLIM is user parameter free, it is easy to use in practical applications. Moreover this paper considers channel equalization and symbol detection for various MIMO transmission schemes, including both space-time block coding and spatial multiplexing, under the challenging channel conditions. The effectiveness of the proposed approaches is demonstrated using in-water experimental measurements recently acquired during WHOI09 and ACOMM10 experiments.     

光纤网络中的云数据并行调度

光纤网络的通信质量受到云数据调度均衡性影响很大，为了改善光纤网络通信质量，提高网络中云数据传输的吞吐量和保真率，通过云数据并行调度，实现云数据传输均衡，提出一种基于自适应判决反馈均衡的光纤网络云数据并行调度模型。构建光纤网络通信的传输信道模型，采用最小均方误差估计方法进行光纤网络的量化融合估计，运用匹配滤波检测器进行云数据中的干扰滤波处理，结合自适应判决反馈均衡方法进行信道均衡，在均衡的信道中对滤波输出的云数据进行并行调度和多线程输出调制。仿真结果表明，采用该方法进行光纤网络中的云数据并行调度的均衡性较好，输出数据的保真率较高，误码率较低，改善了光纤网络的整体性能。     

Diversity reception and equalization techniques for laser communication in space

The principle of band-limited space optical communication system using the techniques of space diversity methods and time domain Rake receiver is analyzed. The joint channel equalizer method combining diversity reception and equalization technique is presented in space laser communication. By computer simulation, the bit error rates of noncoherent pace optical on-off keying signal using different space diversity methods, Rake reception with different inter-symbol interferences, joint diversity equalizations with different signal noise rates and different channel numbers are analysed. The results identify that joint diversity equalization method can enhance space optical communication erformance evidently.     

时间反转滤波器组多载波水声通信方法

针对水声滤波器组多载波(Filter Bank MultiCarrier,FBMC)传统均衡方法效率低、复杂度高等问题,提出了单阵元无源时间反转镜(Passive Time Reversal Mirror,PTRM)和虚拟时间反转镜(Virtual Time Reversal Mirror,VTRM)的均衡算法,该算法利用时间聚焦原理可以降低水声信道带来的符号间干扰(Inter Symbol Interference,ISI),并且不需要在FBMC符号中插入导频,进一步提高了通信速率.通过仿真和外场实验验证了水声FBMC系统采用单阵元PTRM和VTRM的有效性,结果表明,这两种均衡方法能够有效减少ISI,提高系统可靠性能。