基于信道缩短的多频带超宽带信道估计

针对多频带超宽带系统,提出了一种基于信道缩短的信道估计方法.利用循环前缀(CP)结构,在接收机前端设计信道缩短均衡器,解决了循环前缀长度小于信道最大多径延迟时难于估计信道参数的问题.根据均衡器输出序列估计出复合信道,通过反卷积解出原信道参数.计算仿真表明该算法具有良好性能.     

隐含训练序列信道估计中的功率分配

与传统时分发送训练序列的信道估计算法相比,隐含训练序列信道估计算法将训练序列与信息序列直接相加后通过天线发送,从而节约了信道带宽。然而,在天线发送总功率一定时,训练序列的功率越大,信息序列的功率便越小,从而导致信道均衡器的信噪比减小。本文研究了基于MIMO系统的隐含训练序列信道估计算法,分析了信道均衡器信噪比与训练序列功率的关系,并根据均衡器信噪比最大原则推导出训练序列与信息序列的最佳功率分配。分析和仿真结果表明:在训练序列的最佳功率点上,信道均衡器的信噪比最高;随着接收天线信噪比的增加,训练序列的最佳功率增大。     

基于导频序列信道缩短的超宽带信道估计

针对多频带超宽带系统,提出一种基于导频序列信道缩短的信道估计方法,解决了循环前缀长度小于信道最大多径延迟时难于估计信道参数的问题。首先在发送信号中插入块状导频,利用最小均方误差准则（MMSE）,在接收机前端设计信道缩短均衡器,然后根据均衡器输出序列估计出复合信道,最后通过反卷积解出原信道参数。仿真实验表明：该算法具有良好性能。     

MB—OFDM UWB系统中基于信道缩短的信道估计

在MB-OFDM超宽带(UWB)系统中,针对循环前缀(CP)长度小于信道最大多径延迟时难于估计信道参数的问题,提出一种基于信道缩短的信道估计方法。首先采用发送信号插入梳状导频的方式,利用无约束最优准则,在接收机前端设计信道缩短均衡器,然后根据均衡器输出序列估计出复合信道,最后通过反卷积解出原信道参数。仿真结果表明,在信道最大多径延迟大于CP长度时,该算法具有更优的性能。     

莱斯信道的MLSD技术研究

主要研究在时变多径衰落信道下,基于Viterbi算法的最大似然序列检测技术（MLSD）.为了解决MLSD信道估计问题,使均衡器能够及时跟踪信道的变化,提出了基于逐幸存处理（PSP）和最小存活路径（MSP）的MLSD算法.在莱斯信道环境下,对上述算法进行了仿真.仿真结果表明,将RLS-MSP算法与减状态MLSD算法相结合的信道均衡技术具有对复杂信道的强跟踪能力,同时保证了较高的均衡性能和合理的复杂度.     

脉冲超宽带(UWB)跳时系统的频域信道估计

提出一种基于序列和的最大似然(ML)频域信道估计算法,该算法可适用于对跳时UWB通信系统进行基于训练序列的信道估计和盲信道估计,大大简化了运算的复杂性。在仿真中比较了基于理想信道估计、基于一般的频域信道估计以及基于序列和的ML频域信道估计的性能。结果显示,在频域的估计过程中,对发射信号的频谱设定适当的可信门限后,可以获得与直接进行频域信道估计相似的性能。     

基于最佳相关序列的MIMO-OFDM系统信道估计

基于序列相关性提出了一种多输入多输出-正交频分复用(MIMO-OFDM)系统的时域信道估计方法。通过在OFDM符号间插入一种具有最佳相关性的实训练序列,在接收端将接收序列与训练序列作相关运算,得到信道的时域冲激响应。并通过信道阶数估计,进一步提高算法估计性能。理论分析和实验仿真结果均表明,与现有方法相比,本文方法具有更高的系统传输效率和更高的估计性能。     

基于失配序列的OFDM信道估计

本文提出了一种利用失配序列进行OFDM信道估计的新方法。在发端将失配序列加入到OFDM信号中作为训练序列,接收端将收到的去掉循环前缀的失配序列与失配滤波器系数的循环移位序列做相关运算,利用失配序列的优良自相关特性,估计出信道的冲击响应,进而得到子载波处信道的频率响应。理论分析和仿真结果表明,该方法计算复杂度低,估计速度快、精度高,系统的BER性能好。      

基于重要路径捕捉的MIMO-OFDM信道估计方法

基于重要路径捕捉,本文对MIMO-OFDM系统的信道估计方法进行了研究.采用重要路径捕捉,传统的训练序列信道估计方法求逆的运算量和FFT的次数均较大.采用一种改进方法后,可使运算复杂度降低一半,达到了快速性要求.对如何确定信道径数进行了一定分析,并介绍了一种门限值方法以调整路径数.仿真结果表明,改进后的方法降低了运算复杂度,但性能有所下降,即加大了信噪比.     

一种改进的自适应信道估计器

本文提出采用短周期M序列相关算法训练信道估计器,它具有收敛快、精度高、算法简单且与LMS算法相近的优点。训练阶段的相关算法与工作阶段的LMS算法可结合在同一个横向滤波器结构中,从而加速了自适应信道估计器的收敛过程。计算机模拟表明,经四倍的信道响应时间迭代运算后,信道冲激响应样值的估计误差小于0.01。这种自适应信道估计器简单而实用。     

Reduced-state sequence estimation with set partitioning anddecision feedback

A reduced-state sequence estimator for linear intersymbol interference channels is described. The estimator uses a conventional Viterbi algorithm with decision feedback to search a reduced-state subset trellis that is constructed using set-partitioning principles. The complexity of maximum-likelihood sequence estimation (MLSE) due to the length of the channel memory and the size of the signal set is systematically reduced. An error probability analysis shows that a good performance/complexity tradeoff can be obtained. In particular, the results indicate that the required complexity to achieve the performance of MLSE is independent of the size of the signal set for large enough signal sets. Simulation results are provided for two partial-response systems. A simple technique for quadrature partial-response signaling (QPRS) is described that eliminates the quasicatastrophic nature of the ML trellis     

瑞利信道下的自适应MLSE算法

由于在数字移动通信中要考虑多径和多普勒频移的影响,建立了瑞利时变衰落信道模型,并使用最大似然序列检测算法实现均衡.它的基本思想是在MLSE算法中插入信道估计算法,不断跟踪实时信道变化并及时更新.为降低复杂度,研究了传统的自适应MLSE算法和运幸存MLSE两种经典算法,仿真表明,当多普勒频移较小时,两种算法有相近的性能,...     

Estimating Digital Inter-Symbol Interference Channel Blindly Based on the One-Step Branch Transition Rules in Trellises

1 IntroductionChannel estimation without the benefit of a prior known training sequence has attracted increasing attention in recent years. In this paper, a novel discrete-time digital inter-symbol interference (ISI) channel blind estimation sub-optimal a…     

Estimating Digital Inter-Symbol Interference Channel Blindly Based on the One-Step Branch Transition Rules in Trellises

A novel discrete-time digital inter-symbol interference (ISI) channel blind estimation sub-optimal algorithm is proposed. This algorithm reduces the complexity of the optimal maximum likelihood sequence estimation(MLSE) considerably based on the one-step branch transition rules in trellises, and is suitable for the estimation of the channels with small lengths of ISI.     

时变多径MIMO信道中一种带自适应信道追踪的序列检测方法

该文针对时变多径MIMO信道,各MISO子系统首先分别采用混合MLSE(H-MLSE)处理,然后结合-幸存状态选择,提出了一种复杂度可控的、带自适应信道追踪的序列检测方法。与传统的MLSE算法相比,该方法具有3个显著特征:通过参数选择,可实现对算法复杂度的可控调节;通过嵌入的判决导向/LMS(DD/LMS)算法,对各幸存状态转移对应的幸存路径上的信道参数可实现接近零时延追踪;可部分采用并行处理技术来实现。对具有两条多径的2X4 MIMO时变信道通过数值仿真表明:当2时,该方法可获得满意的检测性能。     

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     

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.     

Joint data and channel estimation for TDMA mobile channels

One of the main problems in time-division multiple-access (TDMA) digital mobile communications is the poor performance of the receiver for fast-fading channels. We propose to use a novel joint data and channel estimation (JDCE) technique to improve performance. The basic idea of this method originates from the so-called generalized likelihood ratio (GLR) test of classical detection theory. The technique was combined with the well-known Viterbi algorithm and applied to blind equalization by Seshadri [1]. In this paper, we introduce this technique to TDMA mobile communications. We find that the problems of delayed channel updating and divergence caused by error decision feedback in conventional maximum likelihood sequence estimation with decision delay (MLSE/DD) can be completely solved. An improvement of 4.5 dB can be obtained compared with the MLSE/DD method when it is applied to the Northern American Digital Cellular (NADC) system. The specified IS-54 requirement of 19 dB signal-to-noise ratio (SNR) at a bit error rate (BER) of 3% and a vehicle speed of 100 km/h can be met with a remarkable margin of 8 dB. When the intersymbol interference (ISI) of a channel is not severe, the required computation is moderate. For example, in the NADC system, the computational burden of the JDCE method is twice that of the MLSE/DD method if the least-mean-squares (LMS) algorithm is used for channel tracking.This work was presented in part in the Third International Symposium on Personal, Indoor and Mobile Radio Communications, Boston, MA, October 1992.     

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     

带判决反馈的盲最大似然序列估计

本文提出了一种新型的带有判决反馈的减小状态最大似然序列估计ＲＳＳＤＦＰＳＰ,新算法带有两个信道估值器并且可以工作在盲环境下．使用最大似然序列估计（ＭＬＳＥ）来处理信道冲激响应的前导干扰及主径,反馈滤波器处理后尾干扰,并且用ＰｅｒＳｕｒｖｉｖｉｎｇＰｒｏｃｅｓｉｎｇ（ＰＳＰ）算法来得到ＭＬＳＥ部分的信道冲激响应,信道估值器２得到后尾干扰．计算机模拟表明,这种ＲＳＳＤＦＰＳＰ方案在减小ＭＬＳＥ的计算复杂度的同时能最大限度地得到ＭＬＳＥ的性能,是ＭＬＳＥ在计算复杂度与性能之间的较好折中．