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Signal blind detection method based on double Sigmoid hysteresis noise chaotic neural network

A neural network and blind detection technology, applied in the direction of transmission modification based on link quality, devices dedicated to receivers, shaping networks in transmitters/receivers, etc., can solve problems such as slow convergence of energy functions

Active Publication Date: 2018-09-25
NANJING UNIV OF POSTS & TELECOMM +1
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Problems solved by technology

However, TCHNN has negative self-coupling, which will lead to slower convergence of the energy function

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  • Signal blind detection method based on double Sigmoid hysteresis noise chaotic neural network
  • Signal blind detection method based on double Sigmoid hysteresis noise chaotic neural network
  • Signal blind detection method based on double Sigmoid hysteresis noise chaotic neural network

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[0059] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0060] The present invention proposes the signal blind detection method based on double Sigmoid hysteresis noise chaotic neural network, and its implementation process is as follows:

[0061] When ignoring noise, the receiver equation for a discrete-time channel is defined as

[0062] x N =SΓ T

[0063] (1)

[0064] In the formula, X N is the receiving data matrix, S is the sending signal matrix, Γ is the channel impulse response h jj Constituted block Toeplitz matrix; ( ) T Represents matrix transposition;

[0065] Among them, the sending signal matrix:

[0066] S=[s L+M (k),L,s L+M (k+N-1)] T =[s N (k),L,s N (k-M-L)] N×(L+M+1) ,

[0067] M is the channel order, L is the equaliz...

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Abstract

The invention discloses a signal blind detection method based on a double Sigmoid hysteresis noise chaotic neural network, which is characterized by comprising the following steps: step SS1: constructing a received data matrix XN; step SS2: performing singular value decomposition on the received data matrix XN; Step SS3: setting a weight matrix W; step SS4: selecting an activation function of thedouble Sigmoid hysteresis chaotic neural network, performing the iterative operation of the double Sigmoid hysteresis chaotic neural network, and then substituting the result iterated each time into an energy function E (t) of the double Sigmoid hysteresis noise chaotic neural network; when the energy function E (t) reaches a minimum value, the double Sigmoid hysteresis noise chaotic neural network reaching equilibrium, and ending the iteration. The present invention utilizes a double Sigmoid chaotic neural network and hysteresis noise for the first time to form a double Sigmoid hysteresis noise chaotic neural network, which enhances the network optimization performance and improves the quality of the network optimization solution. The anti-noise performance and the convergence speed of the method in present invention are superior to those of the conventional Hopfield signal blind detection algorithm.

Description

technical field [0001] The invention relates to a signal blind detection method based on a double-Sigmoid hysteresis noise chaotic neural network, and belongs to the technical field of wireless communication signal processing and neural network. Background technique [0002] The rapid development of data communication and wireless sensor network technology has put forward higher requirements for the blind detection of communication signals (BlindDetection). The so-called blind detection means that only the received signal itself can be used to detect the transmitted signal, thereby eliminating inter-signal interference (ISI) to improve the information transmission rate and reliability. [0003] In order to solve the problem of poor signal transmission quality and poor anti-interference ability caused by improving genetic, ant colony, immune, particle swarm and other intelligent algorithms, many literatures have begun to use Hopfield neural network to study the problem of bli...

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Application Information

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IPC IPC(8): H04L25/03H04L1/00
CPCH04L1/0038H04L25/03165
Inventor 张昀于舒娟李冰蕊陈少威杨杰曹建
Owner NANJING UNIV OF POSTS & TELECOMM
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