Channel equalization

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  • The most thorough, up-to-date reference on channel equalization—from basic concepts to complex modeling techniques In today's instant-access society, a high premium is placed on information that can be stored and communicated effectively. As a result, storage densities and communications rates are being pushed to capacity, causing information symbols to interfere with one another.

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  • In this article we examine the problem of communication channel equalization and how it relates to the inversion of a linear system of equations.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Blind Channel Equalization Using Constrained Generalized Pattern Search Optimization and Reinitialization Strategy

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Blind Channel Equalization with Colored Source Based on Constrained Optimization Methods

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Blind Adaptive Channel Equalization with Performance Analysis

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Channel Equalization in Filter Bank Based Multicarrier Modulation for Wireless

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  • One of the earliest andmost successful applications of adaptive filters is adaptive channel equalization in digital communication systems.

    pdf20p nguyen4 17-11-2009 49 5   Download

  • There are many DSP applications that are used in our daily lives, some of which have been introduced in previous chapters. DSP algorithms, such as random number generation, tone generation and detection, echo cancellation, channel equalization, noise reduction, speech and image coding, and many others can be found in a variety of communication systems. In this chapter, we will introduce some selected DSP applications in communications that played an important role in the realization of the systems. ...

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  • State-Space Kalman Filters 7.2 Sample-Adaptive Filters y(m) e(m) µ α w(m) α z –1 wk(m+1) ADAPTIVE FILTERS 7.3 Recursive Least Square (RLS) Adaptive Filters 7.4 The Steepest-Descent Method 7.5 The LMS Filter 7.6 Summary A daptive filters are used for non-stationary signals and environments, or in applications where a sample-by-sample adaptation of a process or a low processing delay is required.

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  • FADING CHANNEL CHARACTERIZATION AND MODELING and hence, following (2.3), the instantaneous SNR per symbol of the channel, , is distributed according to an exponential distribution given by p D 1 exp , ½0 2.7 The MGF corresponding to this fading model is given by M s D 1 s 1 2.8 In addition, the moments associated with this fading model can be shown to be given by 2.9 E[ k ] D  1 C k k where  Ð is the gamma function. The Rayleigh fading model therefore has an AF equal to 1 and typically agrees very well with experimental data for mobile...

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  • CHANNEL EQUALIZATION AND BLIND DECONVOLUTION 15.1 15.2 15.3 15.4 15.5 15.6 15.7 Introduction Blind-Deconvolution Using Channel Input Power Spectrum Equalization Based on Linear Prediction Models Bayesian Blind Deconvolution and Equalization Blind Equalization for Digital Communication Channels Equalization Based on Higher-Order Statistics Summary lind deconvolution is the process of unravelling two unknown signals that have been convolved.

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  • Neural Network Based Equalization In this chapter, we will give an overview of neural network based equalization. Channel equalization can be viewed as a classification problem. The optimal solution to this classification problem is inherentlynonlinear.

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  • Transforming the information source to a form compatible with a digital system. Sampling. Aliasing. Quantization Uniform and non-uniform. Baseband modulation. Binary pulse modulation. M-ary pulse modulation. M-PAM (M-ary Pulse amplitude modulation).Assuming real time transmission and equal energy per transmission data bit for binary-PAM and 4-ary PAM

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  • Adaptive Filters for Blind Equalization 24.1 Introduction 24.2 Channel Equalization in QAM Data Communication Systems 24.3 Decision-Directed Adaptive Channel Equalizer 24.4 Basic Facts on Blind Adaptive Equalization 24.5 Adaptive Algorithms and Notations 24.

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  • In this article we examine the problem of communication channel equalization and how it relates to the inversion of a linear system of equations. Channel equalization is the process by which the effect of a band-limited channel may be diminished, i.e., equalized, at the sink of a communication system.

    pdf10p longmontran 18-01-2010 39 6   Download

  • Practical DSP Applications in Communications There are many DSP applications that are used in our daily lives, some of which have been introduced in previous chapters. DSP algorithms, such as random number generation, tone generation and detection, echo cancellation, channel equalization, noise reduction, speech and image coding, and many others can be found in a variety of communication systems. In this chapter, we will introduce some selected DSP applications in communications that played an important role in the realization of the systems. 9.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Multipass Channel Estimation and Joint Multiuser Detection and Equalization for MIMO Long-Code DS/CDMA Systems

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  • Tham khảo sách 'adaptive filtering', kỹ thuật - công nghệ, điện - điện tử phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • As with every hypothesis test, inference based on alpha estimates can lead to the detection of a lucky fund, namely a fund with a significant estimated alpha, while its true alpha is equal to zero. The difficulty raised by the standard approach is that it implies a multiple hypothesis test since the null hypothesis of no performance is not tested once, but M times. Accounting for the presence of luck in a multiple testing framework is much more complex, because luck cannot be measured by the significance level applied to each fund.

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  • Signal detection in AWGN channels Minimum distance detector. Maximum likelihood. Average probability of symbol error. Union bound on error probability. Upper bound on error probability based on the minimum distance.ISI in the detection process due to the filtering effects of the system Overall equivalent system transfer function

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