TAILIEUCHUNG - Digital Signal Processing Handbook P24

Adaptive Filters for Blind Equalization Introduction Channel Equalization in QAM Data Communication Systems Decision-Directed Adaptive Channel Equalizer Basic Facts on Blind Adaptive Equalization Adaptive Algorithms and Notations Mean Cost Functions and Associated Algorithms The Sato Algorithm • BGR Extensions of Sato Algorithm • Constant Modulus or Godard Algorithms • Stop-and-Go Algorithms • Shalvi and Weinstein Algorithms • Summary A Common Analysis Approach • Local Convergence of Blind Equalizers • Initialization Issues Linearly Constrained Equalizer With Convex Cost Initialization and Convergence of Blind Equalizers Globally Convergent Equalizers Fractionally Spaced Blind Equalizers Concluding Remarks References Zhi. | Zhi Ding. Adaptive Filters for Blind Equalization. 2000 CRC Press LLC. http . Adaptive Filters for Blind Equalization Zhi Ding Auburn University Introduction Channel Equalization in QAM Data Communication Systems Decision-Directed Adaptive Channel Equalizer Basic Facts on Blind Adaptive Equalization Adaptive Algorithms and Notations Mean Cost Functions and Associated Algorithms The Sato Algorithm BGR Extensions of Sato Algorithm Constant Modulus or Godard Algorithms Stop-and-Go Algorithms Shalvi and Weinstein Algorithms Summary Initialization and Convergence of Blind Equalizers A Common Analysis Approach Local Convergence of Blind Equalizers Initialization Issues Globally Convergent Equalizers Linearly Constrained Equalizer With Convex Cost Fractionally Spaced Blind Equalizers Concluding Remarks References Introduction One of the earliest and most successful applications of adaptive filters is adaptive channel equalization in digital communication systems. Using the standard least mean LMS algorithm an adaptive equalizer is a finite-impulse-response FIR filter whose desired reference signal is a known training sequence sent by the transmitter over the unknown channel. The reliance of an adaptive channel equalizer on a training sequence requires that the transmitter cooperates by often periodically resending the training sequence lowering the effective data rate of the communication link. In many high-data-rate bandlimited digital communication systems the transmission of a training sequence is either impractical or very costly in terms of data throughput. Conventional LMS adaptive filters depending on the use of training sequences cannot be used. For this reason blind adaptive channel equalization algorithms that do not rely on training signals have been developed. Using these blind algorithms individual receivers can begin self-adaptation without transmitter assistance. This ability of .

TỪ KHÓA LIÊN QUAN
Đã phát hiện trình chặn quảng cáo AdBlock
Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.