一种用于数字QAM接收机的盲均衡器实现

提出了一种适用于数字QAM接收机的自适应盲均衡器实现方案。该均衡器采用多模算法(MMA)和最小均方算法(LMS)，称为MMA—LMS算法结合判决反馈结构(DFE)，即采用前向滤波器和反馈滤波器两级滤波器组实现，提高了信道的适应性能和降低均衡器的阶数。仿真结果表明，该均衡器比一般采用恒模算法(CMA)的横式均衡器有更好的性能，更易于硬件实现。     

基于LMS算法的光纤信道自适应均衡器的研究

文章分析了影响光纤通信系统性能的主要因素,介绍了在理论分析的基础上建立的单模光纤信道仿真模型;还介绍了判决反馈均衡器(DFE)的基本结构和最小均方(LMS)误差算法,并用MATLAB软件仿真了采用LMS算法对10 Gbit/s光纤信道进行自适应均衡的性能.仿真结果表明,LMS算法简单、高效,能有效消除光纤色散和偏振模色散(PMD)引起的码间干扰,比较适合对均衡速度要求较高的光纤通信系统.     

基于DVB-C的QAM接收机均衡技术研究与设计

介绍了一种基于DVB-C标准的QAM接收机均衡器方案.笔者首先介绍了均衡器的横向滤波器与判决反馈均衡器结构,其次阐述了几种常用的均衡算法,并对算法切换问题作了研究,然后综合结构和算法的优越性,选择采用DFE与盲均衡相结合的方式,在输入存在多径干扰和加性噪声的情况下,使用经典常数模算法(CMA)和面向判决的最小均方误差算法(DD-LMS),切换部分采用误差值切换,利用Matlab对16QAM,64QAM信号进行仿真,最后实现了QAM接收机对均衡器的要求.     

Turbo均衡在移动通信中的研究

为消除符号间的干扰，Douillard．et．a1提出了turbo均衡这种迭代均衡和解码的方法。在Prokis’B信道下，对Turbo均衡的性能进行仿真，采用基于MMSE的线性均衡(MMSE—LE)和判决反馈均衡(MMSE—DFE)算法，以及appMMSE—LE／SIC算法，通过性能仿真，对不同算法、不同的迭代次数对系统性能的影响进行比较。结果表明:appMMSE—LE／SIC算法在复杂性几乎没有增加的情况下，性能提高了很多。     

应用于10 Gbit/s光通信及背板传输的自适应均衡器设计

描述了一种既可用于背板传输也可用于光纤通信的高速串行收发器前端均衡器的设计。为适应光信号在传播中的色散效应,使用前馈均衡器(FFE)加判决反馈均衡器(DFE)的组合,取代了背板通信中常用的连续时间线性均衡器(CTLE)和DFE的组合。设计使用3 pre-tap、3 post-tap和1个main tap的抽头组合方式,兼顾pre-cursor和post-cursor的信号失真,有效补偿范围为15 dB。补偿系数采用完全自适应算法调整,对FFE采用模拟MSE算法调整,DFE引擎采用1/16速率数字sign-sign最小均方差(LMS)算法实现。芯片使用UMC 28 nm工艺流片,输入信号频率为10 Gbit/s。     

适于卫星信道的混合式自适应迭代均衡算法

针对卫星信道的线性失真特性,本文在分析基于因子图的迭代均衡算法和传统线性均衡器特性的基础上,提出了一种混合式迭代均衡算法(HIE,Hybrid Iterative Equalization),以较小的复杂度代价改善均衡器的性能。HIE均衡器由横向线性滤波器和迭代均衡器串行级联组成,结合了线性均衡器的简单性与迭代均衡算法的准确性,并根据最小均方误差原则和递推型统计逼近法自适应调整线性均衡器与迭代均衡器的参数。仿真结果表明,在典型群时延信道和8PSK映射方式下,HIE的信噪比门限与AWGN信道门限仅差0.2dB,要优于最小均方误差(LMS)线性均衡器0.5dB。     

严格采样FMT系统频域均衡算法

根据滤波多音调制(FMT)系统基本原理推导了严格采样FMT系统的矩阵表示式,并针对严格采样FMT系统符号间干扰(ISI)严重的问题,根据最小均方误差准则提出了一种子信道频域均衡算法,给出了新算法的最小二乘迭代实现.仿真结果表明,新算法能有效均衡FMT信号,相比较于判决反馈均衡(DFE)算法,新算法的实现结构更简单并且误码率性能更好.     

并行级联频域判决反馈(DFE)均衡策略及不匹配译码

该文提出了一种使用并行级联的turbo类型判决反馈均衡器(DFE)结构,用于抑制线性码间干扰。基于该结构又提出了一种低复杂度的基于频域迭代块判决反馈检测(Turbo Block DFE, TBDFE)算法。该并行策略比传统的块迭代判决反馈均衡算法(Iterative Block DFE, IBDFE)有更大的性能增益,且在信道估计存在相同的一定误差时,该结构与IBDFE的性能增益比没有估计误差时更大。该文还研究了SNR估计误差对TBDFE算法与传统IBDFE算法的影响,结果表明二者对SNR的过估计比欠估计均为敏感。     

一种用于数字QAM解调芯片中的盲均衡器实现

设计了一种用于数字QAM解调芯片中的自适应盲均衡器。该均衡器采用常模算法（CMA）和判决引导最小均方算法（DD-LMS）,结合判决反馈结构（DFE）,即采用前向滤波和反馈滤波两级滤波器组,该方案提高了信道的自适应性能和降低了均衡器的阶数。通过对算法的简化和电路结构的优化,减少了硬件资源,降低了电路的面积和功耗。     

自适应均衡在无线通信中的应用

在无线通信中,由于信道的不完善性和多径衰落等因素,使得数据在无线信道上传输时码间干扰严重,基于LMS(最小均方)算法的自适应判决反馈均衡器因其实现复杂度小可以广泛应用,文章对其原理和结构进行了分析并通过Matlab对该均衡器性能进行了计算机仿真,结果表明:在接收端采用该自适应均衡器可以大大降低误比特率,在工程上具有一定参考价值。     

A Maximum-Likelihood Sequence Estimator with Decision-Feedback Equalization

A decision-feedback equalizer (DFE) is proposed as a prefilter which limits the complexity of a maximum-likelihood sequence estimator (MLSE) implemented by the Viterbi algorithm (VA) for channels having a long impulse response. By imbedding a DFE into the structure of the MLSE, the overall impulse response of the system is truncated to a short duration. With this practical receiver, a compromise may be made between performance and complexity by properly choosing the duration of a desired impulse response. A technique is also developed to estimate the performance of the receiver numerically, taking into account the effect of incorrect decision feedback on the VA. Analysis and computer simulation over a single-pole channel show that the proposed scheme can reduce the complexity of the MLSE while retaining much of its performance advantages.     

Adaptive combined DFE/MLSE techniques for ISI channels

By embedding a decision-feedback equalizer (DFE) into the structure of a maximum-likelihood sequence estimator (MLSE), an adaptive combined DFE/MLSE scheme is proposed. In this combined DFE/MLSE, the embedded DFE has three functions: (i) prefiltering the received signals and truncating the equivalent channel response into the desired one, (ii) compensating for channel distortions, and (iii) providing the MLSE detector with predicted values of input signals. Since the embedded MLSE detector operates on the predicted signals the detected symbols at the output of the DFE/MLSE do not suffer any delay and can be directly fed back into the embedded DFE so that the error propagation, which usually takes place in a conventional DFE, can be greatly reduced. Analytical and simulation results indicate that the performance is significantly improved by the DFE/MLSE compared to the conventional DFE while its computation complexity is much less than that of the conventional MLSE receiver. The combined DFE/MLSE can use different adaptive structures (block-updating, sliding window updating or symbol-by-symbol updating) to meet different performance objectives. Moreover, the proposed DFE/MLSE provides a trade-off between performance and complexity with a parameter m representing the MLSE detection depth as well as the number of predicting steps of the embedded DFE. For some particular values of m, this scheme is capable of emulating the conventional DFE, MLSE-VA, adaptive LE-MLSE equalizer, adaptive DDFSE, and adaptive BDFE without detection delay     

Optimized Delay Diversity for Suboptimum Equalization

The optimization of delay diversity (DD) for linear equalization (LE) and decision-feedback equalization (DFE) is presented. The general case of transmission over a correlated multiple-input–multiple-output frequency-selective fading channel is considered. The proposed optimization requires the knowledge of the statistical properties of the wireless channel at the transmitter, but channel state information is only required at the receiver side. Based on an approximation of the bit error rate for LE and DFE, a stochastic gradient algorithm for optimization of the DD transmit filters is derived. Simulation results for the Global System for Mobile Communications (GSM)/Enhanced Data Rates for GSM Evolution system show significant performance gains of the proposed optimized DD scheme over the DD schemes reported by Gore (Proc. IEEE Inter. Conf. Commun., 2002) and Hehn (IEEE Trans. Wireless Commun., vol. 4, p. 2289, 2005) if LE and DFE are used at the receiver.     

Application of Sequential Estimation Algorithm Based on Branch Metric to Frequency-Selective Channel Equalization

In this work, a sequential estimation algorithm based on branch metric is used as channel equalizer to combat intersymbol interference in frequency-selective wireless communication channels. The bit error rate (BER) and computational complexity of the algorithm are compared with those of the maximum likelihood sequence estimation (MLSE), the recursive least squares (RLS) algorithm, the Fano sequential algorithm, the stack sequential algorithm, list-type MAP equalizer, soft-output sequential algorithm (SOSA) and maximum-likelihood soft-decision sequential decoding algorithm (MLSDA). The BER results have shown that whilst the sequential estimation algorithm has a close performance to the MLSE using the Viterbi algorithm, its performance is better than the other algorithms. Beside, the sequential estimation algorithm is the best in terms of computational complexity among the algorithms mentioned above, so it performs the channel equalization faster. Especially in M-ary modulated systems, the equalization speed of the algorithm increases exponentially when compared to those of the other algorithms.     

A new adaptive equalizer with channel estimator for mobile radiocommunication

For unknown mobile radio channels with severe intersymbol interference (ISI), a maximum likelihood sequence estimator, such as a decision feedback equalizer (DFE) having both feedforward and feedback filters, needs to handle both precursors and postcursors. Consequently, such an equalizer is too complex to be practical. This paper presents a new reduced-state, soft decision feedback Viterbi equalizer (RSSDFVE) with a channel estimator and predictor. The RSSDFVE uses maximum likelihood sequence estimation (MLSE) to handle the precursors and truncates the overall postcursors with the soft decision of the MLSE to reduce the implementation complexity. A multiray fading channel model with a Doppler frequency shift is used in the simulation. For fast convergence, a channel estimator with fast start-up is proposed. The channel estimator obtains the sampled channel impulse response (CIR) from the training sequence and updates the RSSDFVE during the bursts in order to track changes of the fading channel. Simulation results show the RSSDFVE has nearly the same performance as the MLSE for time-invariant multipath fading channels and better performance than the DFE for time-variant multipath fading channels with less implementation complexity than the MLSE. The fast start-up (FS) channel estimator gives faster convergence than a Kalman channel estimator. The proposed RSSDFVE retains the MLSE structure to obtain good performance and only uses soft decisions to subtract the postcursor interference. It provides the best tradeoff between complexity and performance of any Viterbi equalizers     

面向ASIC设计的单载波超宽带信道均衡研究

单载波超宽带通信系统的均衡在芯片实现中面临高吞吐率、高性能和低复杂度三方面问题。该文首先比较了最大似然均衡（MLSE）、线性均衡（LE）、判决反馈均衡（DFE）及单载波频域均衡（SC-FDE）在性能、复杂度及高速化实现上的优缺点,并综合考虑SC-UWB系统这一特殊的应用场景最终选择了DFE。然后针对DFE算法中的三个关键参数——前馈阶数Nf,反馈阶数Nb及判决延迟D,提出了一种实际系统中有效且实用的参数优化设计策略,最后仿真证明了优化策略的实用性和有效性。     

MLSE and MAP Equalization for Transmission Over Doubly Selective Channels

In this paper, equalization for transmission over doubly selective channels is discussed. The symbol-by-symbol maximum a posteriori probability (MAP) equalizer and the maximum-likelihood sequence estimation (MLSE) are discussed. The doubly selective channel is modeled using the basis expansion model (BEM). Using the BEM allows for an easy and low-complexity mechanism for constructing the channel trellis to implement the MLSE and the MAP equalizer. The MLSE and the MAP equalizer are implemented for single-carrier transmission and for multicarrier transmission implemented using orthogonal frequency-division multiplexing (OFDM). In this scenario, a complexity-diversity tradeoff can be observed. In addition, we propose a joint estimation and equalization technique for doubly selective channels. In this joint estimation and equalization technique, the channel state information (CSI) is obtained in an iterative manner. Simulation results show that the performance of the joint channel estimation and equalization approaches the performance when perfect CSI is available at the receiver.     

基于最大比特速率准则的FMT频域均衡算法

针对严格采样滤波多音调制(Filtered Multi-Tone modulation,FMT)系统符号间干扰严重的问题,通过推导严格采样FMT系统的矩阵表示式,提出了一种新的频域均衡算法,该算法以最大化比特速率为目标来确定均衡器的系数。仿真结果表明,在ATTC(Advanced Television Technology Center)E型信道条件下,新算法的可达比特率(Achievable Bit Rate,ABR)性能比最小均方误差准则的线性均衡和判决反馈均衡分别提高了40%和70%,误码率性能在BER为10-4则分别改善了3 dB和5 dB;而在WLAN信道条件下,新算法的ABR性能分别提高了45%和75%,误码率性能在BER为10-4则分别改善了6 dB和10 dB。     

Iterative Equalization using Improved Block DFE for Synchronous CDMA Systems

Iterative equalization using optimal multiuser detector and optimal channel decoder in coded CDMA systems improves the bit error rate (BER) performance tremendously. However, given large number of users employed in the system over multipath channels causing significant multiple-access interference (MAI) and intersymbol interference (ISI), the optimal multiuser detector is thus prohibitively complex. Therefore, the sub-optimal detectors such as low-complexity linear and non-linear equalizers have to be considered. In this paper, a novel low-complexity block decision feedback equalizer (DFE) is proposed for the synchronous CDMA system. Based on the conventional block DFE, the new method is developed by computing the reliable extrinsic log-likelihood ratio (LLR) using two consecutive received samples rather than one received sample in the literature. At each iteration, the estimated symbols by the equalizer is then saved as a priori information for next iteration. Simulation results demonstrate that the proposed low-complexity block DFE algorithm offers good performance gain over the conventional block DFE.     

Adaptive Bayesian decision feedback equalizer for dispersive mobileradio channels

The paper investigates adaptive equalization of time-dispersive mobile radio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE