The achievable bit error rate of a linear equalizer is crucially determined by the choice of a decision delay parameter. This brief paper presents a simple method for the efficient determination of the optimal decisio...The achievable bit error rate of a linear equalizer is crucially determined by the choice of a decision delay parameter. This brief paper presents a simple method for the efficient determination of the optimal decision delay parameter that results in the best bit error rate performance for a linear equalizer. Keywords Linear equalizer - decision delay - bit error rate Eng Siong Chng received his university education at the University of Edinburgh, Edinburgh, Scotland (BEng 1991, PhD 1995). After his PhD, he spent 6 months in Japan, working as a researcher for Riken. After working in industry in Singapore for 7 years, he joined the School of Computer Engineering, Nanyang Technological University in 2003. His research interests are in digital signal processing for communication applications, speech and handwriting recognition and noise reduction.Sheng Chen obtained a BEng degree in control engineering from the East China Petroleum Institute, Dongying, China, in 1982, and a PhD degree in control engineering from the City University at London in 1986. He joined the School of Electronics and Computer Science at the University of Southampton in September 1999. He previously held research and academic appointments at the Universities of Sheffield, Edinburgh and Portsmouth. Dr Chen is a Senior Member of the IEEE in the USA. His recent research works include adaptive nonlinear signal processing, modeling and identification of nonlinear systems, neural networks and machine learning, finite-precision digital controller design, evolutionary computation methods and optimization. He has published over 200 research papers. In the database of the world’s most highly cited researchers in various disciplines, compiled by the Institute for Scientific Information (ISI) of the USA, Dr Chen is on the list of highly cited researchers in the category that covers all branches of engineering subject, see www.ISIHighlyCited.com.展开更多
由于卫星通信系统中高功率放大器的非线性失真与多径信道的线性衰落效应相互耦合会引发传输性能恶化问题,而传统的盲均衡算法(如恒模算法)虽在应对多径引起的线性符号间干扰时具有一定效果,但无法有效补偿高阶调制信号中的非线性失真,...由于卫星通信系统中高功率放大器的非线性失真与多径信道的线性衰落效应相互耦合会引发传输性能恶化问题,而传统的盲均衡算法(如恒模算法)虽在应对多径引起的线性符号间干扰时具有一定效果,但无法有效补偿高阶调制信号中的非线性失真,尤其是在没有训练序列的盲均衡场景下,传统方法难以提供足够的监督信息.为了克服这一挑战,本文提出一种基于判决重构的非线性卫星信道盲均衡算法(blind Equalization Algorithm for Nonlinear satellite channels based on Decision-Reconstruction,DR-NEA),DR-NEA采用判决-插值-重构模式生成参考信号,从而实现无监督条件下的非线性与线性失真联合补偿.首先,算法通过恒模算法对接收到的信号进行线性均衡,消除多径效应引起的线性失真.随后,通过判决、插值和重构生成参考信号,该参考信号为非线性均衡器参数辨识提供监督信息.最后,DR-NEA使用拟牛顿法在最小均方误差准则下对Wiener型均衡器的参数进行辨识,进而实现对信道中的线性与非线性失真联合补偿.仿真结果表明,在高阶调制模式(32APSK、32QAM、64QAM)下,DR-NEA相较于传统线性均衡算法,显著提升了性能,在误码率为1×10-3时,较传统线性均衡算法性能增益超4 dB,体现了该算法在高阶调制下较强的非线性补偿能力.此外,当判决误码率低于9.44%时,DR-NEA依旧保持稳定且输出性能几乎不受影响,进一步验证了本文算法的鲁棒性.DR-NEA通过创新性地引入基于判决重构的参考信号生成方法,解决了传统盲均衡算法无法提供足够监督信息的问题.同时,采用拟牛顿法进行Wiener模型参数辨识,实现了高效的非线性均衡器优化.实验结果验证了该算法在非线性与线性失真补偿方面的优越性能,特别适用于高阶调制信号的传输.综上所述,DR-NEA算法有效解决了卫星通信中非线性失真与多径衰落的联合干扰问题,具有重要的理论意义和广泛的实际应用前景,特别是在高速率、高阶调制的卫星通信场景中,能够显著提升系统的传输性能.展开更多
研究了水声图像高速传输信号处理方法,它包括两个方面,一方面是水声相干通信信号处理方法,其中:(1)多普勒频移补偿,在数据包的前后两端插入已知线性调频(Chirp)信号,拷贝相关后求互相关,估计相对多普勒平均频移。在自适应判决反馈均衡...研究了水声图像高速传输信号处理方法,它包括两个方面,一方面是水声相干通信信号处理方法,其中:(1)多普勒频移补偿,在数据包的前后两端插入已知线性调频(Chirp)信号,拷贝相关后求互相关,估计相对多普勒平均频移。在自适应判决反馈均衡器中加上自适应相位补偿器,采用快速自优化最小均方(LMS)算法,与其对应的速度容限优于常用的二阶锁相环相位补偿器的。两种补偿方法联合工作时,性能优良。(2)带有分集合并器的自适应判决反馈均衡器的算法是快速自优化的LMS算法,计算量小,性能优良。(3)自适应判决反馈均衡器与Turbo-网格编码调制(TCM)译码器级连、迭代算法。研究了基于软输出维特比(SOVA)方法的新型的比特-符号转换器,用它时误比特率(BER)比常规编码、映射方法的近似小2个数量级。另一方面是抗误码的图像压缩方法。本文基于数字小波变换和定长编码方法,研究了声图像的压缩。它包括:(1)选用CDF9/7小波进行小波变换。(2)对小波系数子带能量进行统计分析,三层小波分解是合适的。(3)对不同能量的子带采用不同的量化步长。(4)采用定长编码算法。结果表明声图像压缩比特率为0.85。当BER小于10^(-3)时,图像质量完好。当BER小于10^(-2)时,图像中出现少量小黑白点。在上述基础上研制了水声通信机,频带为(7.5～12.5)kHz,接收声呐阵为8基元等距线阵,信号为QPSK和8PSK。在中国千岛湖进行了湖试,采用SOVA硬迭代算法,达到了低BER。传输一幅256×256×8的声图需时约7s。传输距离与传输速率之积为55 km kbps。展开更多
文摘The achievable bit error rate of a linear equalizer is crucially determined by the choice of a decision delay parameter. This brief paper presents a simple method for the efficient determination of the optimal decision delay parameter that results in the best bit error rate performance for a linear equalizer. Keywords Linear equalizer - decision delay - bit error rate Eng Siong Chng received his university education at the University of Edinburgh, Edinburgh, Scotland (BEng 1991, PhD 1995). After his PhD, he spent 6 months in Japan, working as a researcher for Riken. After working in industry in Singapore for 7 years, he joined the School of Computer Engineering, Nanyang Technological University in 2003. His research interests are in digital signal processing for communication applications, speech and handwriting recognition and noise reduction.Sheng Chen obtained a BEng degree in control engineering from the East China Petroleum Institute, Dongying, China, in 1982, and a PhD degree in control engineering from the City University at London in 1986. He joined the School of Electronics and Computer Science at the University of Southampton in September 1999. He previously held research and academic appointments at the Universities of Sheffield, Edinburgh and Portsmouth. Dr Chen is a Senior Member of the IEEE in the USA. His recent research works include adaptive nonlinear signal processing, modeling and identification of nonlinear systems, neural networks and machine learning, finite-precision digital controller design, evolutionary computation methods and optimization. He has published over 200 research papers. In the database of the world’s most highly cited researchers in various disciplines, compiled by the Institute for Scientific Information (ISI) of the USA, Dr Chen is on the list of highly cited researchers in the category that covers all branches of engineering subject, see www.ISIHighlyCited.com.
文摘由于卫星通信系统中高功率放大器的非线性失真与多径信道的线性衰落效应相互耦合会引发传输性能恶化问题,而传统的盲均衡算法(如恒模算法)虽在应对多径引起的线性符号间干扰时具有一定效果,但无法有效补偿高阶调制信号中的非线性失真,尤其是在没有训练序列的盲均衡场景下,传统方法难以提供足够的监督信息.为了克服这一挑战,本文提出一种基于判决重构的非线性卫星信道盲均衡算法(blind Equalization Algorithm for Nonlinear satellite channels based on Decision-Reconstruction,DR-NEA),DR-NEA采用判决-插值-重构模式生成参考信号,从而实现无监督条件下的非线性与线性失真联合补偿.首先,算法通过恒模算法对接收到的信号进行线性均衡,消除多径效应引起的线性失真.随后,通过判决、插值和重构生成参考信号,该参考信号为非线性均衡器参数辨识提供监督信息.最后,DR-NEA使用拟牛顿法在最小均方误差准则下对Wiener型均衡器的参数进行辨识,进而实现对信道中的线性与非线性失真联合补偿.仿真结果表明,在高阶调制模式(32APSK、32QAM、64QAM)下,DR-NEA相较于传统线性均衡算法,显著提升了性能,在误码率为1×10-3时,较传统线性均衡算法性能增益超4 dB,体现了该算法在高阶调制下较强的非线性补偿能力.此外,当判决误码率低于9.44%时,DR-NEA依旧保持稳定且输出性能几乎不受影响,进一步验证了本文算法的鲁棒性.DR-NEA通过创新性地引入基于判决重构的参考信号生成方法,解决了传统盲均衡算法无法提供足够监督信息的问题.同时,采用拟牛顿法进行Wiener模型参数辨识,实现了高效的非线性均衡器优化.实验结果验证了该算法在非线性与线性失真补偿方面的优越性能,特别适用于高阶调制信号的传输.综上所述,DR-NEA算法有效解决了卫星通信中非线性失真与多径衰落的联合干扰问题,具有重要的理论意义和广泛的实际应用前景,特别是在高速率、高阶调制的卫星通信场景中,能够显著提升系统的传输性能.
文摘研究了水声图像高速传输信号处理方法,它包括两个方面,一方面是水声相干通信信号处理方法,其中:(1)多普勒频移补偿,在数据包的前后两端插入已知线性调频(Chirp)信号,拷贝相关后求互相关,估计相对多普勒平均频移。在自适应判决反馈均衡器中加上自适应相位补偿器,采用快速自优化最小均方(LMS)算法,与其对应的速度容限优于常用的二阶锁相环相位补偿器的。两种补偿方法联合工作时,性能优良。(2)带有分集合并器的自适应判决反馈均衡器的算法是快速自优化的LMS算法,计算量小,性能优良。(3)自适应判决反馈均衡器与Turbo-网格编码调制(TCM)译码器级连、迭代算法。研究了基于软输出维特比(SOVA)方法的新型的比特-符号转换器,用它时误比特率(BER)比常规编码、映射方法的近似小2个数量级。另一方面是抗误码的图像压缩方法。本文基于数字小波变换和定长编码方法,研究了声图像的压缩。它包括:(1)选用CDF9/7小波进行小波变换。(2)对小波系数子带能量进行统计分析,三层小波分解是合适的。(3)对不同能量的子带采用不同的量化步长。(4)采用定长编码算法。结果表明声图像压缩比特率为0.85。当BER小于10^(-3)时,图像质量完好。当BER小于10^(-2)时,图像中出现少量小黑白点。在上述基础上研制了水声通信机,频带为(7.5～12.5)kHz,接收声呐阵为8基元等距线阵,信号为QPSK和8PSK。在中国千岛湖进行了湖试,采用SOVA硬迭代算法,达到了低BER。传输一幅256×256×8的声图需时约7s。传输距离与传输速率之积为55 km kbps。
