基于混合遗传算法及最低误码率准则的几何特征均衡器

针对高效调制通信系统中带内干扰抑制问题,提出一种基于最低误码率准则的非线性几何特征均衡器,并用径向基函数神经网络来实现.为优化非线性均衡器的参数训练,本文构造了一种新的遗传随机梯度混合算法.仿真表明:对于扩展的二元相移键控信号,在相对强的窄带干扰下,匹配滤波器及线性均衡器已失效,而基于最低误码率准则的几何特征均衡器仍能表现出良好的性能,也大大优于基于最小均方误差准则的非线性均衡器.     

低计算复杂度的多级软判决均衡器用于干扰抑制

针对通信系统中非线性均衡器的计算复杂度问题,提出一种基于最低误码率准则的多级软判决均衡器.通过调节各级均衡器的拒绝门限,可使多级软判决均衡器在计算复杂度与误码率之间取得折衷.仿真表明:对于扩展的二元相移键控信号,在相对强的调频信号和调幅信号的联合干扰下,匹配滤波器和基于最小均方误差准则的线性均衡器已失效,而基于最低误码率准则的多级软判决均衡器仍能表现出良好的性能,并且大大减少计算复杂度.此外,还发现一个有趣的结果,即多个不同的非线性均衡器的协作可获得比单个均衡器更优的性能.     

基于Hammerstein模型的非线性信道广义线性盲均衡算法

为了提高非线性信道盲均衡的性能、降低运算复杂度,本文以Hammerstein模型代替传统的Volterra级数模型来模拟非线性信道,利用非线性信道接收信号呈现非圆性的特点,构造了一种新的基于Wiener非线性模型的广义线性盲均衡器,并在常模准则的基础上提出了NCWL-CMA和NCWL-CMA Newton-like两种非线性信道广义线性盲均衡器抽头系数更新算法.理论分析和仿真实验结果表明,与传统盲均衡算法相比,新算法显著地降低了剩余码间干扰,提高了收敛速度.     

一种用于QAM解调信号的LMS自适应均衡器

设计了一种用于QAM(Quadrature Amplitude Modulation)解调信号的LMS自适应均衡器。此均衡器采用线性自适应算法中的最小均方算法(LMS)．其结构由线性横向滤波器和需要训练序列的滤波器抽头系数更新模块组成．它可实现16／64／256点的QAM解调。利用MATLAB／Simulink对LMS自适应均衡器的收敛速度、误码率等指标进行仿真模拟,仿真结果表明,此LMS自适应均衡器对通过非理想信道的QAM传输信号具有较好的均衡性能。     

基于最低误码率准则及Volterra序列的几何特征均衡器

针对通信系统中带内噪声及干扰的滤波问题,提出一种基于最低误码率准则的非线性几何特征均衡器.考虑到噪声与干扰具有不同的统计特性,本文所建立的均衡模型把匹配滤波器输出映射到特征空间,使信号与干扰在特征空间形成不同的几何特征,再用Volterra序列予以分离,而分离是基于最低误码率准则.仿真表明:对于扩展的二元相移键控信号,在相对强的调频信号、调幅信号及噪声的联合干扰下,匹配滤波器和基于最小均方误差准则的线性均衡器均已失效,而几何特征均衡器仍能表现出良好的性能,并且基于奇次三阶Volterra序列的几何特征均衡器更具实用价值.     

基于最低误码率准则的奇次三阶Volterra均衡器用于干扰抑制

该文针对通信系统中的干扰抑制问题,提出一种基于最低误码率准则的Volterra均衡器.区别于以往研究中采用最小均方误差准则估计Volterra核,本文采用最低误码率准则.仿真表明:对于扩展的二元相移键控信号,在相对强的窄带干扰下,匹配滤波器和基于最小均方误差准则的线性均衡器已失效,而基于最低误码率准则的Volterra均衡器仍能表现出良好的性能,也大大优于最小均方误差准则的Volterra均衡器;并且在计算复杂度与误码率性能的权衡中,奇次三阶Volterra均衡器更有实用价值.     

最低误码率非线性均衡器的快速自适应学习算法

针对最低误码率非线性均衡器的参数在线自适应学习问题,本文提出基于拟牛顿方法的快速自适应学习算法。采用Parzen窗函数方法估计误码率,通过设定切换条件,使参数学习在滑窗随机梯度法与滑窗拟牛顿法之间切换。这既增加了新算法的数值稳定性,又可提高收敛速度。通过对拟牛顿方法进行修改,还使新算法既可以在线自适应学习,也可用于高维参数的快速学习。仿真采用最低误码率非线性均衡器对通信系统进行干扰抑制和信道均衡,结果表明了新算法的高效性。     

一种新的群时延自适应均衡算法

为了补偿无线信道群时延特性所带来的影响,基于盲均衡器结构提出了一种优化的自适应均衡算法.该算法是基于最小均方误差准则的变步长LMS算法,通过自适应调节判决反馈均衡器两组横向滤波器的抽头系数,实现对信道群时延的自适应补偿.通过对16-QAM信号的仿真结果表明,新算法能够对有效补偿信道群时延带来的影响,并且明显降低系统的误码率.     

Receiving-end Equalization Scheme for Nonlinear Channel Based on Wiener Model

针对功率放大器的非线性问题和多径信道引起的时间色散,提出了一种接收端联合补偿方案.由基于维纳模型的均衡器实现,该均衡器由一个线性滤波器级联一个多项式滤波器组成,所有运算均在接收端完成.仿真结果显示,经过非线性均衡的误码率性能比无非线性均衡时有很大改善,在同一误码率下,信噪比至少降低了3.5 dB.     

基于多小波双变换的非线性卫星信道盲均衡算法

针对非线性卫星信道Volterra盲均衡系统收敛缓慢、计算复杂高等不足,提出了基于多小波双变换的非线性卫星信道盲均衡算法.该算法用Wiener均衡器代替Volterra均衡器,减小了均衡器结构的复杂性;用平衡正交多小波对Wiener均衡器的输入信号进行变换,降低了输入信号的自相关性;在Wiener均衡器输出端增加一级判决反馈滤波器,同时对其输入信号作平衡多小波变换,又降低了判决反馈滤波器输出信号的自相关性.仿真结果验证了该算法的有效性.     

Finite dictionary techniques for MSER equalization in RKHS

Adaptive channel equalization is a signal processing technique to mitigate inter-symbol interference in a time dispersive channel. For adaptive equalization, minimum mean square error (MMSE) criterion-based reproducing kernel Hilbert spaces (RKHS) approaches such as the kernel least mean squares (KLMS) algorithm and its variants have been suggested in the literature for nonlinear channels. Another optimality criterion, based on minimum bit/symbol error rate (MBER/MSER), is a better choice for adapting an equalizer as compared to MMSE criterion. A kernel-based minimum symbol error rate (KMSER) equalization algorithm combines minimum symbol error rate (MSER)-based approaches with RKHS techniques. However, most algorithms in RKHS such as KMSER/KLMS require infinite storage requirement and hence cannot be practically implemented. To curtail the infinite memory requirement, and make adaptive algorithm suitable for implementation with finite memory and processing power, we propose quantized KMSER (QKMSER) and fixed-budget quantized KMSER (FBQKMSER)-based equalizers in this paper. In this paper, we derive the dynamical equation for MSE evolution of the QKMSER and FBQKMSER and find their performance to be asymptotically close to the MSE behavior of the KMSER. Also, it is found via simulations that the tracking performance of FBQKMSER is better than all the compared algorithms in this paper which is particularly useful for non-stationary channels.     

A maximum-likelihood bound approaching minimum bit error rate linear multiuser detector for frequency-selective DS-CDMA systems

In this letter, a minimum bit error rate (MBER) linear multiuser detector (MUD) is considered for direct-sequence code-division multiple-access (DS-CDMA) communication systems, distorted by time-varying and frequency-selective multipath fading channels. Based on the approach for finding filter coefficients of the proposed MBER MUD, an efficient Newton method with a barrier parameter is developed. The BER performance of the MBER MUD is compared to other conventional detectors. The study finds that the proposed MBER MUD has more than 2 dB gain over the linear minimum mean-squared error (LMMSE) detector. Furthermore, in the high SNR region, the BER performance of the proposed MBER MUD approaches the performance of the maximum-likelihood (ML) detector.     

Blind equalizer for constant-modulus signals based on Gaussian process regression

A new blind equalization method for constant modulus (CM) signals based on Gaussian process for regression (GPR) by incorporating a constant modulus algorithm (CMA)-like error function into the conventional GPR framework is proposed. The GPR framework formulates the posterior density function for weights using Bayes' rule under the assumption of Gaussian prior for weights. The proposed blind GPR equalizer is based on linear-in-weights regression model, which has a form of nonlinear minimum mean-square error solution. Simulation results in linear and nonlinear channels are presented in comparison with the state-of-the-art support vector machine (SVM) and relevance vector machine (RVM) based blind equalizers. The simulation results show that the proposed blind GPR equalizer without cumbersome cross-validation procedures shows the similar performances to the blind SVM and RVM equalizers in terms of intersymbol interference and bit error rate.     

Adaptive minimum bit-error-rate filtering

Adaptive filtering has traditionally been developed based on the minimum mean square error (MMSE) principle and has found ever-increasing applications in communications. The paper develops adaptive filtering based on an alternative minimum bit error rate (MBER) criterion for communication applications. It is shown that the MBER filtering exploits the non-Gaussian distribution of filter output effectively and, consequently, can provide significant performance gain in terms of smaller bit error rate (BER) over the MMSE approach. Adopting the classical Parzen window or kernel density estimation for a probability density function (pdf), a block-data gradient adaptive MBER algorithm is derived. A stochastic gradient adaptive MBER algorithm is further developed for sample-by-sample adaptive implementation of the MBER filtering. Extension of the MBER approach to adaptive nonlinear filtering is also discussed.     

Approximate Minimum BER Power Allocation for MIMO Spatial Multiplexing Systems

Precoding for multiple-input multiple-output (MIMO) spatial multiplexing generally requires high feedback overhead and/or high-complexity processing. Simultaneous reduction in transmitter complexity and feedback overhead is proposed by imposing a diagonal structural constraint to precoding, i.e., power allocation. Minimum bit-error rate (MBER) is employed as the optimization criterion, and an approximate MBER (AMBER) power-allocation algorithm is proposed for a variety of receivers, including zero-forcing (ZF), successive interference cancellation (SIC), and ordered SIC (OSIC). While previously proposed precoding schemes either require ZF equalization for MBER, or use a minimum mean-squared error (MMSE) criterion, we provide a unified MBER solution to power allocation for ZF, SIC, and OSIC receiver structures. Improved error-rate performance is shown both analytically and by simulation. Simulation results also indicate that SIC and OSIC with AMBER power allocation offer superior performance over previously proposed MBER precoding with ZF equalization, as well as over MMSE precoding/decoding. Performance under noisy channels and power feedback is analyzed. A modified AMBER algorithm that mitigates error propagation in interference cancellation is developed. Compared with existing precoding methods, the proposed schemes significantly reduce both transmit processing complexity and feedback overhead, and improve error-rate performance     

Nonlinear equalization of multipath fading channels withnoncoherent demodulation

A nonlinear decision-based adaptive equalizer compatible with differentially coherent phase shift keying (PSK) is proposed for frequency-selective fading channels. This equalization scheme is appropriate whenever conventional equalizers are not capable of tracking phase variations in selective fading channels. The received signal is first converted to a baseband signal and then sent through a differential detector. A nonlinear processor before the equalizer generates the needed nonlinear terms that are weighted and summed in the equalizer. Nonlinear intersymbol interference at the output of the differential detector is dealt with by minimizing an error signal between the output of the equalizer and the detected data. The adaptation algorithm can be any algorithm currently used for conventional equalizers. Our simulation results confirm that for channels with spectral nulls, equalization is achieved successfully with the proposed scheme, whereas, linear equalizers, either with coherent or noncoherent detection, fail     

An efficient low-complexity technique for MLSE equalizers for linear and nonlinear channels

In this paper, a novel sequence equalizer, which belongs to the family of cluster-based sequence equalizers, is presented. The proposed algorithm achieves the maximum likelihood solution to the equalization problem in a fraction of computational load, compared with the classic maximum likelihood sequence estimation (MLSE) equalizers. The new method does not require the estimation of the channel impulse response. Instead, it utilizes the estimates of the cluster centers formed by the received observations. Furthermore, a new cluster center estimation scheme, which exploits the intrinsic dependencies among the cluster centers, is proposed. The new center estimation method exhibits enhanced performance with respect to convergence speed, compared with an LMS-based channel estimator. Moreover, this gain in performance is obtained at substantially lower computational load. The method is also extended in order to cope with nonlinear channels. The performance of the new equalizer is tested with several simulation examples, using both the quadrature phase shift keying (QPSK) and the 16-quadrature amplitude modulated (QAM) signaling schemes for linear and nonlinear communication channels.