适用于慢衰落信道的联合最大似然序列估计

为了克服移动通信中存在的多径衰落效应,基于Viterbi算法的最大似然序列估计需要不断跟踪信道参数的变化.在建立有限带宽信号的多径衰落信道传输模型的基础上,提出了一种适合于慢衰落信道特性的降阶联合信道跟踪与最大似然均衡算法D-PSP.该方法根据信道特性选择不同的降阶维数N,在保证数据正确接收率的条件下,不同程度的有效减小PSP算法的计算时间和存储空间.     

稀疏信道下基于稀疏贝叶斯学习的精简星座盲均衡算法

针对稀疏信道的盲均衡问题,在精简星座均衡算法框架下建立线性模型,利用稀疏信道下均衡器固有的稀疏特性,引入具有稀疏促进作用的先验分布对均衡器系数加以约束,使用稀疏贝叶斯学习方法迭代求解均衡器系数得到最大后验估计值。该文提出的均衡方法属于数据复用类均衡算法的范畴,能够适用于数据较短的应用场合。与随机梯度方法相比,算法性能受均衡器长度影响较小,收敛后误符号率性能更好,仿真实验验证了算法的有效性。     

平坦衰落信道下基于变分贝叶斯的多天线信号联合符号检测算法

该文针对平坦衰落信道下存在信道参数差异的多天线接收信号联合参数估计和符号检测问题,提出一种基于变分贝叶斯的联合处理算法。算法直接利用多个接收数据流进行信息符号的估计,抑制传统信号合成与解调解耦处理带来的性能损失。将问题建模为已知多组观测数据条件下发送符号、信道传输时延、信道增益和噪声功率的联合最大后验估计问题。基于变分贝叶斯理论对该最大后验进行近似求解,在相对熵最小化的准则下,推导得到了各个待估参数解析形式的近似后验分布——变分分布。所提算法无需计算各参数精确的点估计值,而是采用信道参数和信息符号变分分布迭代处理的方式进行联合求解。仿真结果表明,所提算法通过多信号、多参数的联合处理能够获得优于经典解耦处理和部分联合处理技术的系统误码率性能,且在接收天线数目较多和观测数据长度较短时性能优势体现更加明显。     

基于ML的多径信道下直扩信号PN码和信道的联合盲估计

针对非协作通信下多径信道直接序列扩频信号伪随机(PN,Pseudorandom)码的估计难题,本文在分析信号二阶统计特性的基础上,提出了一种基于最大似然(ML,Maximum Likelihood)的PN码和多径信道联合盲估计的方法.该方法首先建立PN码和信道序列的最大似然数学模型,然后通过交替转换数学模型和使用迭代最小二乘投影(ILSP,Iterative Least Square Projection)算法实现PN码和多径信道的联合估计.为了进一步降低算法复杂度和避免矩阵求逆,本文给出了算法的自适应求解方式.此外,针对低信噪比下信道估计误差引起PN码的估计精度下降的问题,本文提出了一种基于迭代总体最小二乘投影的改进算法.所提算法不受PN码码型限制,并通过仿真实验验证了算法的有效性.     

一种改进的SC-FDE块迭代判决反馈均衡器

单载波频域均衡(SC-FDE)系统能有效地对抗频域选择性衰落信道。块迭代判决反馈均衡(IBDFE)是单载波频域均衡系统中一种有效的非线性均衡算法,但是其计算复杂度较高,且复杂度与迭代次数成正比。鉴于此,提出一种改进算法,在改进算法中,将接收信号经过MMSE均衡得到发送信号估计值作为传统算法反馈支路输出,前馈支路结构保持不变。对比传统IBDFE算法,迭代被取消,故降低了计算复杂度。对传统IBDFE算法和改进IBDFE算法性能进行比较,实验结果证明:改进IBDFE算法性能在与传统IBDFE算法性能相似的条件下降低了计算复杂度。     

用概率推导和加权迭代L1范数实现信号重构

在对信号稀疏性统计分析的基础上,将具有稀疏描述能力的拉普拉斯分布用于描述信号的先验分布,基于贝叶斯法,利用信号采样值、拉普拉斯先验分布和高斯似然模型,推导信号的后验概率密度估计;最后将最大后验概率(MAP)估计过程转化为加权迭代L1范数的最小化问题。在求解过程中,与非加权的L1范数法进行对比表明,信号重构性能明显提高;通过实验计算,详细讨论了其中一些参数的取值原则和范围;针对稀疏度不同的信号,随着信号非零点数的增加,本文算法重构结果明显优于基追踪(BP)和(OMP)法;与同类的IRL1算法相比较,本文算法更具普遍性和理论意义。     

多调制指数CPM频域均衡技术研究

研究了多指数连续相位调制(multi-h CPM)信号的频域均衡技术.区别于采用循环前缀(CP)的作为保护间隔,提出了带有训练序列(TS)的发射帧结构设计.在理想信道估计和同步的条件下,采用最大似然估计(MLSE)维特比译码,仿真了多径衰落信道中不同均衡算法的误码率性能,并比较了采用CP和TS帧结构的mutli-h CPM频域均衡技术在误码率性能上的差异.     

稀疏多径信道中单载波频域均衡系统的联合干扰抵消和均衡

针对大时延稀疏多径信道条件下的单载波频域均衡系统(SC-FDE),本文提出了一种新的迭代均衡结构,记为IC-FDE-DFE。IC-FDE-DFE可以缩短频域均衡所需的循环前缀(CP)的长度,提高稀疏多径信道下SC-FDE系统的带宽效率和功率效率。与单纯的时频域混合判决反馈均衡器(H-DFE)相比,IC-FDE-DFE能够充分利用信道的‘稀疏’特性,其中的均衡器具有较低的运算复杂度和设计复杂度。仿真结果表明,IC-FDE-DFE能够在迭代过程中逐渐抵消大时延多径的影响,逼近H-DFE的理想性能。     

CPM调制的迭代检测技术

CPM信号具有包络恒定、峰均比小、功率利用率高的优点。针对CPM信号的特征,比较了最大似然序列检测Viterbi算法和逐符号的最大后验概率(MAP)解调算法的特点,重点研究了使用迭代检测技术进行软输入输出解调及译码的MAP解调方法。在AWGN信道条件下对CPM全响应和部分响应信号进行了误码性能仿真,结果表明采用相干解调和译码,迭代检测方案可得到较高的编码增益。     

一种基于STBC的SC-FDE系统双天线联合检测算法

针对双发双收的基于空时分组编码的单载波频域均衡(STBC-SC-FDE)系统,研究了一种双天线联合检测算法,通过信道估计、导频干扰消除、加权联合均衡等过程重构发送的信息。并根据信噪比对频域信号与均衡矩阵进行加权处理,使联合检测算法适用于2路接收信号信噪比不同的情况。仿真结果表明,所提出的双天线联合检测算法在SUI-3信道下最大可获得约8dB的接收分集增益。与时域最大比合并(MRC)算法相比,由于采用联合检测,该算法能有效改善系统在多径衰落信道下的误码率。     

双选信道下OCDM系统低复杂度均衡

正交Chirp复用(OCDM)是近年来提出的一种新的多载波体系,通过菲涅尔变换,获得一组正交Chirp信号,实现了CSS的最大频谱效率。该文介绍了OCDM系统的基本原理,重点研究了OCDM系统的低复杂度均衡算法。双选信道下,传统的MMSE均衡算法性能下降,提出一种基于近似带状矩阵的阻尼LSQR算法,作为求解稀疏矩阵的最小二乘迭代算法。为了缓解快速时变信道中的ICI,提出一种基于近似带状矩阵的LSQR-BDFE算法,结合判决反馈均衡,通过LSQR算法迭代计算。仿真结果表明,双选信道下,OCDM系统比OFDM系统有着更好的BER性能,所提出的LSQR-BDFE算法和带状阻尼LSQR算法,比MMSE均衡算法有着性能优势。     

Space-time turbo equalization in frequency-selective MIMO channels

A computationally efficient space-time turbo equalization algorithm is derived for frequency-selective multiple-input-multiple-output (MIMO) channels. The algorithm is an extension of the iterative equalization algorithm by Reynolds and Wang (see Signal Processing, vol.81, no.5, p.989-995, 2001) for frequency-selective fading channels and of iterative multiuser detection for code-division multiple-access (CDMA) systems by Wang and Poor (see IEEE Trans. Commun., vol.47, p.1046-1061, 1999). The proposed algorithm is implemented as a MIMO detector consisting of a soft-input-soft-output (SISO) linear MMSE detector followed by SISO channel decoders for the multiple users. The detector first forms a soft replica of each composite interfering signal using the log likelihood ratio (LLR), fed back from the SISO channel decoders, of the transmitted coded symbols and subtracts it from the received signal vector. Linear adaptive filtering then takes place to suppress the interference residuals: filter taps are adjusted based on the minimum mean square error (MMSE) criterion. The LLR is then calculated for adaptive filter output. This process is repeated in an iterative fashion to enhance signal-detection performance. This paper also discusses the performance sensitivity of the proposed algorithm to channel-estimation error. A channel-estimation scheme is introduced that works with the iterative MIMO equalization process to reduce estimation errors.     

Iterative frequency-domain channel estimation and equalization for single-carrier transmissions without cyclic-prefix

Compared to conventional time-domain equalization, frequency-domain equalization (FDE) presents a computationally efficient alternative for the reception of single carrier (SC) transmissions. In this paper, we consider iterative FDE (IFDE) with explicit frequency-domain channel estimation (FDCE) for non-cyclic-prefixed SC systems. First, an improved IFDE algorithm is presented based on soft iterative interferencecancellation. Second, a new adaptive FDCE (AFDCE) algorithm based on per-tone Kalman filtering is proposed to track and predict the frequency-domain channel coefficients. The AFDCE algorithm employs across-tone noise reduction, exploits temporal correlation between successive blocks, and adaptively updates the auto-regressive model coefficients, bypassing the need for prior knowledge of channel statistics. Finally, block-overlapping is used to facilitate the joint operation of IFDE and AFDCE. Simulation results show that, compared to related IFDE and adaptive channel estimation schemes, the proposed schemes offer lower mean-square error (MSE) in channel prediction, lower bit error rate (BER) after decoding, and robustness to non-stationary channels.     

Computationally efficient methods for blind decision feedback equalization of QAM signals

This paper investigates computationally efficient methods for blind decision feedback equalization (DFE) that reduce the complexity and power requirements of blind equalization algorithms while maintaining their steady-state characteristics for quadrature amplitude modulation (QAM) signals. These include the power-of-two error (POT), selective coefficient update (SCU), and frequency-domain block (FDB) methods. A novel radius-directed stop-and-go (RSG) method is introduced, which selectively adjusts the equalizer tap coefficients based on the equalizer output radius. In addition, a new activation/de-activation method based on the equalizer output radius is utilized to control the feedback equalizer (FBE) of the DFEs. Simulation studies and analysis are provided for empirically derived cable and microwave channels and Ricean fading channels.     

MAXIMUM LIKELIHOOD SOURCE SEPARATION FOR FINITE IMPULSE RESPONSE MULTIPLE INPUT—MULTIPLE OUTPUT CHANNELS IN THE PRESENCE OF ADDITIVE NOISE

Blind identification-blind equalization for finite Impulse Response(FIR)Multiple Input-Multiple Output(MIMO)channels can be reformulated as the problem of blind sources separation.It has been shown that blind identification via decorrelating sub-channels method could recover the input sources.The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators,which decorrelate the output signals of subchannels,and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix.In this paper,a new qpproximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed.The proposed method outperforms BIDS in the presence of additive white Garssian noise.     

Frequency-Domain Set-Membership Filtering and Its Applications

Frequency-domain adaptive filtering is appealing in many applications, particularly channel equalization. This paper presents frequency-domain set-membership filtering (F-SMF) and derives adaptive algorithms for F-SMF. The F-SMF is employed to design single-carrier frequency-domain equalizer (SC-FDE). With an unconventional parameter-dependent error-bound specification, an F-SMF algorithm is derived and shown to provide superior performance with sparse updates of parameter estimates. Exploring the feature of sparse updates, we present an innovative parallel adaptive architecture that shares the updating processors and that finds natural appeal in frequency-domain diversity combining and equalization for very dispersive fading channels like those found in broadband wireless communications     

Turbo均衡在Non-CP SC-FDE系统中的应用

针对无循环前缀的单载波频域均衡(Non-CP SC-FDE)系统中的符号间干扰(ISI),提出了一种Turbo均衡与循环重构联合迭代算法。通过循环重构技术消除ISI,并采用频域均衡频域判决反馈(FDE-FDDF)方式,将Turbo均衡算法与循环重构算法结合在一起进行迭代。仿真结果表明,Non-CP SC-FDE系统使用该算法能实现与传统有CP系统近似的误码性能,在大幅提高传输效率的同时能够有效消除多径干扰。     

A hybrid adaptive blind equalization algorithm for QAM signals in wireless communications

A hybrid adaptive channel equalization technique for quadrature amplitude modulation (QAM) signals is proposed. The proposed algorithm, which is referred to as the modified constant modulus algorithm (MCMA), minimizes an error cost function that includes both amplitude and phase of the equalizer output. In addition to the amplitude-dependent term that is provided by the conventional constant modulus algorithm (CMA), the cost function includes an additive signal constellation matched error (CME) term. This term can be designed to satisfy a set of desirable properties. The MCMA is compared with the CMA for blind equalization. The performance is measured for wireless channels using both transient and steady-state behavior of the mean square error (MSE). It is shown that MCMA is superior and more robust in low signal-to-noise ratio (SNR) environments. Simulation results demonstrate that using MCMA improves adaptive channel equalization by increasing the convergence rate and decreasing the steady-state mean square error.     

用于稀疏系统辨识的低复杂度集员NLMS算法

针对现有的稀疏集员(SM,Set-Membership)自适应滤波算法,普遍存在稳态均方偏差(MSD,Mean Square Deviation)的稳定性较低及运算复杂度较高等问题,提出了一种新颖的稀疏集员NLMS(NLMS,Normalized Least Mean Square)算法.该方案提出一种运算复杂度更低的...