共查询到18条相似文献,搜索用时 140 毫秒
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针对时变水声信道估计和均衡问题,该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法.基于叠加训练序列方案,将训练序列和符号线性叠加,使得训练序列和符号信道信息一致;基于最小二乘算法,进行信道估计.基于频域训练序列干扰消除技术,在频域消除训练序列对符号的干扰;基于频域线性最小均方误差(LMMSE)均衡算法,通过先验、后验、外均值和方差的计算,实现低复杂度信道均衡(符号估计);基于Turbo均衡算法,软重构叠加训练序列和更新信道估计,进行均衡器和译码器的信息交换,利用编码冗余信息,大幅度提升信道均衡性能.进行仿真、水池静态通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率4.8 ksym/s,训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率3 ksym/s,训练序列和符号的功率比为0.25:1),仿真和试验结果验证了所提算法的有效性. 相似文献
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水声信道均衡中基于信道估计的均衡方法理论上具有更优的均衡性能,但较高的计算复杂度限制了算法的实际应用。针对这一问题,该文首先基于Kalman滤波和Turbo均衡提出一种迭代Kalman均衡器,实现了基于软符号的迭代信道估计与迭代Kalman均衡,且复杂度较常规方法降低约1个数量级。其次,针对单一均衡算法和单一方向Turbo均衡器存在的误差传递现象,设计了基于迭代Kalman均衡器与改进成比例归一化LMS (IPNLMS)自适应均衡器相结合的混合双向Turbo均衡器,提高了自适应均衡器的收敛速度和均衡性能,并通过双向均衡结构带来的增益改善了符号估计误差传递的现象。理论分析与仿真实验验证了该文算法的有效性。 相似文献
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针对时变水声信道估计和均衡问题,该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法。基于叠加训练序列方案,将训练序列和符号线性叠加,使得训练序列和符号信道信息一致;基于最小二乘算法,进行信道估计。基于频域训练序列干扰消除技术,在频域消除训练序列对符号的干扰;基于频域线性最小均方误差(LMMSE)均衡算法,通过先验、后验、外均值和方差的计算,实现低复杂度信道均衡(符号估计);基于Turbo均衡算法,软重构叠加训练序列和更新信道估计,进行均衡器和译码器的信息交换,利用编码冗余信息,大幅度提升信道均衡性能。进行仿真、水池静态通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率4.8 ksym/s,训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz,带宽6 kHz,采样频率96 kHz,符号传输速率3 ksym/s,训练序列和符号的功率比为0.25:1),仿真和试验结果验证了所提算法的有效性。 相似文献
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针对Turbo编码MIMO/OFDM系统,本文提出一种低复杂度的Turbo均衡算法,均衡器采用性能近于最优检测的概率数据辅助(Probabilistic Data Association)算法,与软输入软输出的Turbo信道解码器之间迭代交换外信息,实现信道均衡与信道解码的迭代更新,以充分利用已获得的信息,克服传统判决反馈均衡器误差传播的缺陷。仿真表明,该均衡算法性能要比MMSE+MF线性检测算法提高约1dB,在Eb/No为4dB时误比特率达到10-6,且算法复杂度仅为O(N3),经两次迭代就可获得较为满意的码间干扰消除效果。 相似文献
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双选择信道下OFDM系统中一种基于新Kalman滤波估计的Turbo均衡 总被引:1,自引:0,他引:1
在OFDM系统中,信道的快速时变性破坏了子载波间的正交性,从而导致子载波间干扰(ICI),降低了系统性能。该文针对双选择信道的时变特性,提出了一种新的Kalman滤波信道估计算法,将其应用于过采样的复指数基扩展模型(OCE-BEM),从而将一个OFDM符号周期内信道参数时变的问题转化为参数时不变问题,同时,将这种新的Kalman滤波器与基于ICI抑制的低复杂度LMMSE Turbo均衡器相结合,并辅以循环冗余码校验(CRC)控制算法迭代次数,从而不需要更多的导频符号,在保证算法性能的基础上,减小算法的计算时延和复杂度。理论分析和仿真结果表明,该文给出的方法在双选择信道下能够有效地跟踪信道变化并抑制ICI影响。 相似文献
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Turbo均衡是一种通过反复均衡和信道译码来提高接收性能的迭代接收机算法。通常的Turbo均衡算法采用均衡与软输出译码的迭代运算,由于均衡和译码的重复计算,使得复杂度大大提高。文中提出了2种降低复杂度的Turbo均衡器:第一种采用软判决维特比译码,第二种采用软输入硬输出的维特比译码。通过仿真表明,这2种算法在几乎没有损失接收性能的情况下,大大降低了计算复杂度,并且第二种的性能要好于第一种。 相似文献
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针对高阶部分响应连续相位调制(CPM)信号均衡中存在的复杂度高和性能较差等问题,该文从Rimoldi分解的角度出发,设计了一种新的适用于倾斜相位CPM信号的发射帧结构,并在此基础上结合单载波频域均衡(FDE)和Turbo均衡的思想,提出了一种适用于高阶CPM信号的Turbo频域均衡算法。该算法通过将信号均衡转化到频域进行处理,避免了时域均衡算法在计算均衡器系数时存在的大矩阵求逆问题,同时使用Turbo均衡的软信息迭代处理来改善系统的性能。理论分析和仿真结果表明,对于四阶部分响应CPM信号,在存在严重符号间干扰的多径衰落信道的条件下,该算法与现有的基于符号的频域均衡算法相比,在保持较低复杂度的同时,具有大约1.5 dB的性能增益。 相似文献
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Turbo均衡是一种将Turbo原理和均衡技术结合起来的技术。他通过反复均衡和信道译码来提高接收机性能。针时瑞利衰落信道,采用基于线性滤波器的软输入/软输出均衡器来消除码间干扰,其系数由最小均方误差准则确定。译码器采用最大后验概率算法时卷积码译码。考虑到瑞利衰落信道为随机信道,用非相干检测时信道进行估计。接收机通过联合均衡和译码以充分利用已经获得的信息,实现信道估计及信道均衡与信道译码的迭代更新。仿真结果表明其性能不仅远远优于非迭代系统.而且在信噪比高于4dB时几乎可以完全消除符号间干扰的影响,与MAPSE相比其复杂度大大降低。 相似文献
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Turbo均衡应用在水声通信中的问题主要在于水声信道时间扩展长,多接收阵元处理复杂度较高。该文研究了将时间反转与马尔可夫链蒙特卡罗(MCMC)均衡联合优化算法用于实现Turbo均衡。首先进行时间反转实现多接收阵元较长多径时延的压缩,再利用白化滤波器解决时间反转造成的噪声模型失配问题,最后利用复杂度较低的MCMC均衡器结合软迭代信道估计对时间反转合并后得到的信号进行均衡。结合真实实验信道条件对信道响应估计的误差建立模型,通过仿真比较得出, 该算法在相同条件下相对于多阵元直接自适应Turbo均衡算法复杂度降低67%,且有1.6 dB的误码率性能增益。通过对湖上试验数据进行处理,进一步验证了该算法的优势。 相似文献
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This letter proposes a new iterative ISI equalization algorithm that offers low computational complexity: order L2 with channel memory length L. The proposed algorithm is an extension of Reynolds and Wang's SC/MMSE (soft canceler followed by MMSE filter) equalizer: approximations are used properly to reduce the computational complexity. It is shown that the approximations used in the proposed algorithm do not cause any serious performance degradations from the trellis-based iterative equalization algorithms 相似文献
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In this paper, a doubly iterative receiver is proposed for joint turbo equalization, demodulation, and decoding of coded binary continuous-phase modulation (CPM) in multipath fading channels. The proposed receiver consists of three soft-input soft-output (SISO) blocks: a front-end soft-information-aided minimum mean square error (MMSE) equalizer followed by a CPM demodulator and a back-end channel decoder. The MMSE equalizer, combined with an a priori soft-interference canceler (SIC) and an a posteriori probability mapper, forms a SISO processor suitable for iterative processing that considers discrete-time CPM symbols which belong to a finite alphabet. The SISO CPM demodulator and the SISO channel decoder are both implemented by the a posteriori probability algorithm. The proposed doubly iterative receiver has a central demodulator coupled with both the front-end equalizer and the back-end channel decoder. A few back-end demodulation/decoding iterations are performed for each equalization iteration so as to improve the a priori information for the equalizer. As presented in the extrinsic information transfer (EXIT) chart analysis and simulation results for different multipath fading channels, this provides not only faster convergence to low bit error rates, but also lower computational complexity. 相似文献
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In this paper, a doubly-iterative linear receiver, equipped with a soft-information aided frequency domain minimum mean-squared error (MMSE) equalizer, is proposed for the combined equalization and decoding of coded continuous phase modulation (CPM) signals over long multipath fading channels. In the proposed receiver architecture, the front-end frequency domain equalizer (FDE) is followed by the soft-input, softoutput (SISO) CPM demodulator and channel decoder modules. The receiver employs double turbo processing by performing back-end demodulation/decoding iterations per each equalization iteration to improve the a priori information for the front-end FDE. As presented by the computational complexity analysis and simulations, this process provides not only a significant reduction in the overall computational complexity, but also a performance improvement over the previously proposed iterative and noniterative MMSE receivers. 相似文献
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The design of decision feedback equalizers (DFEs) is typically based on the minimum mean square error (MMSE) principle as this leads to effective adaptive implementation in the form of the least mean square algorithm. It is well-known, however, that in certain situations, the MMSE solution can be distinctly inferior to the optimal minimum symbol error rate (MSER) solution. We consider the MSER design for multilevel pulse-amplitude modulation. Block-data adaptive implementation of the theoretical MSER DFE solution is developed based on the Parzen window estimate of a probability density function. Furthermore, a sample-by-sample adaptive MSER algorithm, called the least symbol error rate (LSER), is derived for adaptive equalization applications. The proposed LSER algorithm has a complexity that increases linearly with the equalizer length. Computer simulation is employed to evaluate the proposed alternative MSER design for equalization application with multilevel signaling schemes. 相似文献