The mean-square delayed decision feedback sequence detector

Magarini, Maurizio; Spalvieri, A.; Tartara, G.

doi:10.1109/tcomm.2002.802561

Cited by 16 publications

(8 citation statements)

References 21 publications

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“…From H c (n)S(n) = h δ , it can be seen that the length of equivalent equalizer must be infinite to achieve complete compensation [22,23], which is more suitable for DFE because of the denominator item in (4). Normally, the compensation effects of DFE can be measured by the residual mean square error (MSE) shown as (5), in which λ = σ 2 n /σ 2 x , σ 2 x and σ 2 n are the variances of x(n) and channel noises.…”

Section: Fig 1 Baseband Model Of Dfementioning

confidence: 99%

Self-adjusting decision feedback equalizer for variational underwater acoustic channel environments

Luo

Liu

et al. 2014

J. of Syst. Eng. Electron.

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Aimed at the abominable influences to blind equalization algorithms caused by complex time-space variability existing in underwater acoustic channels, a new self-adjusting decision feedback equalization (DFE) algorithm adapting to different underwater acoustic channel environments is proposed by changing its central tap position. Besides, this new algorithm behaves faster convergence speed based on the analysis of equalizers' working rules, which is more suitable to implement communications in different unknown channels. Corresponding results and conclusions are validated by simulations and spot experiments.

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Section: Fig 1 Baseband Model Of Dfementioning

confidence: 99%

Self-adjusting decision feedback equalizer for variational underwater acoustic channel environments

Luo

Liu

et al. 2014

J. of Syst. Eng. Electron.

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“…In contrast, ANNs can form arbitrarily nonlinear decision boundaries to take up complex classification tasks [7]. Model the nonlinear phenomenon in channel equalization based on ANNs is attractive for imitating the computational function of systems using simple computation in the biological domain [8].…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of blind and non-blind channel equalizers using artificial neural networks

Ranhotra

Kumar

Magarini

et al. 2017

2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN)

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Abstract-In digital communication systems, multipath propagation induces Inter Symbol Interference (ISI).To reduce the effect of ISI different channel equalization algorithms are used. Complex equalization algorithms allow for achieving the best performance but they do not meet the requirements for implementation of real-time detection at low complexity, thus limiting their application. In this paper, we present different blind and non-blind equalization structures based on Artificial Neural Networks (ANNs) and, also, we analyze their complexity versus performance. The simulated network is based on multilayer feedforward perceptron ANN, which is trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network to improve the convergence speed. Simulation results demonstrate that the implementation of equalizers using ANN provides an upper hand over the performance and computational complexity with respect to conventional methods.

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“…In the DFE of Monsen, which is known as the Minimum Mean Square Error DFE (MMSE-DFE), the FFF and the FBF optimize the Mean Square Error (MSE) between the input to the detector and the transmitted constellation symbol. Note that it has been recently observed in [2] that the FFF and the FBF of the MMSE-DFE allow maximum likelihood sequence detection with minimum number of states, and that they can also be adopted in suboptimal sequence detection schemes such as the delayed decision feedback sequence detector of [3]. Although the scope of the present paper is limited to decision feedback equalization, it is clear that the observation of [2] broadens the applicability of the MMSE-DFE FFF and FBF, thus encouraging interest to the very classical research topic of efficient computation of the two filters.…”

Section: Introduction Decision Feedback Equalization Is a Simple Amentioning

confidence: 77%

Computing the feedback filter of the decision feedback equalizer at the FFT speed

Spalvieri

Magarini

Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.

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Abstract-This paper is devoted to the efficient computation of the feedback and feedforward filters of the decision feedback equalizer. The key operation in the computation of the filters is the spectral factorization. The paper proposes to adopt the efficient method for spectral factorization due to Kolmogoroff, which runs at the speed of FFT. Since the duration in the time domain of the impulse response obtained from the spectral factorization is not under the control of the designer, truncation of that impulse response becomes necessary. We propose a method that mitigates the performance loss due to the truncation, and validate the proposal by computer simulation on a variety of channels.

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The mean-square delayed decision feedback sequence detector

Cited by 16 publications

References 21 publications

Self-adjusting decision feedback equalizer for variational underwater acoustic channel environments

Self-adjusting decision feedback equalizer for variational underwater acoustic channel environments

Performance comparison of blind and non-blind channel equalizers using artificial neural networks

Computing the feedback filter of the decision feedback equalizer at the FFT speed

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