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Adaptive Zero Attraction Factor Blind Decision Feedback Equalization Algorithm with Sparse Constraint

A technology of decision feedback equalization and sparse constraints, which is applied to baseband system components, baseband systems, shaping networks in transmitters/receivers, etc.

Active Publication Date: 2021-12-07
HARBIN INST OF TECH AT WEIHAI
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There is still a lack of 0 MMA-DFE Research of Norm Constraints

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  • Adaptive Zero Attraction Factor Blind Decision Feedback Equalization Algorithm with Sparse Constraint
  • Adaptive Zero Attraction Factor Blind Decision Feedback Equalization Algorithm with Sparse Constraint
  • Adaptive Zero Attraction Factor Blind Decision Feedback Equalization Algorithm with Sparse Constraint

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Embodiment 1

[0100] Compare 1 among the present invention below 0 - Performance of MMBDFE-AZA and existing methods. During the simulation, the underwater acoustic channel is generated using the Bellhop model, which is a ray acoustic model based on the Gaussian beam tracking method. In this model, the carrier frequency is 15khz, the distance between the transmitter and receiver is set to 1000m, the transmitter is located at a depth of 5m, the receiver is located at a depth of 10m, the sound velocity is set to 1540-1543, and the wave height is set to 0.2m. The modulation method adopts QPSK. In (22) and (23), μ f and μ b Both are set to 0.005. The symbol transmission rate is 4000 bits / second, the tap length of FFF is set to 59, and the tap length of FBF is set to 45. The tap coefficient vector of FFF initializes the center tap to be 1 and the other taps to zero, while the tap coefficient vector of FBF is initialized to all zero values. The variables used in the proposed algorithm are in...

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Abstract

The invention relates to the technical field of underwater acoustic communication, in particular to an adaptive zero-attractor blind judgment with sparse constraints that can improve the ability to identify sparse systems and adjust the zero-attractor according to the power of the measurement noise Feedback equalization algorithm, the cost function of the algorithm can be described as follows J(n)={E[|y r (n)| 2 -R r ] 2 +E[|y i (n)| 2 -R i ] 2}+λ||f(n)|| 0 (60), where y r (n) and y i (n) denote the real and imaginary parts of the blind equalization output, respectively. R r and R i respectively represent the statistical information corresponding to the transmitted signal q(n), λ≥0 represents the regularization parameter; ||f(n)|| 0 l representing the tap coefficient vector 0 Norm used to count the number of nonzero weight coefficients in the vector.

Description

Technical field: [0001] The invention relates to the technical field of underwater acoustic communication, in particular to an adaptive zero-attractor blind judgment with sparse constraints that can improve the ability to identify sparse systems and adjust the zero-attractor according to the power of the measurement noise Feedback equalization algorithm. Background technique: [0002] Due to the complexity and variability of the underwater acoustic environment, the underwater acoustic channel (UAC) is by far one of the most challenging wireless channels. The characteristic of UAC is that it has a sparse multipath structure and a large delay spread time, and the energy is mainly concentrated in a small part of the channel duration. [0003] In order to reduce intersymbol interference, equalization techniques are usually used for suppression. Compared with the traditional non-blind equalization, the blind equalization does not require a training sequence, and uses the statis...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L25/03H04B13/02
CPCH04B13/02H04L25/03891H04L25/03949
Inventor 刘志勇柯淼李博谭周美
Owner HARBIN INST OF TECH AT WEIHAI
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