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Robust multi-user detection method based on quantum Hopfield neural network and quantum fish swarm algorithm

A multi-user detection and neural network technology, applied in biological neural network models, computing, computing models, etc.

Inactive Publication Date: 2014-05-14
HARBIN ENG UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to address the shortcomings of the existing robust multi-user detection method in the impact noise environment, and proposes a robust multi-user detection method based on quantum Hopfield neural network and quantum fish swarm algorithm that can achieve optimal detection performance in a shorter time. stick multi-user detection method

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  • Robust multi-user detection method based on quantum Hopfield neural network and quantum fish swarm algorithm
  • Robust multi-user detection method based on quantum Hopfield neural network and quantum fish swarm algorithm
  • Robust multi-user detection method based on quantum Hopfield neural network and quantum fish swarm algorithm

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] In this method, a quantum neural network is firstly designed to obtain an approximate solution, and a quantum fish swarm algorithm is proposed to solve the discrete optimization problem. In engineering applications, when the characteristic index of the shock noise is 2, the shock noise satisfies the Gaussian noise distribution function form, so the method proposed in the present invention can also solve the multi-user detection problem in the Gaussian noise environment. It also has good detection results in the case of noise.

[0038] The present invention is achieved through the following technical solutions, mainly comprising the following steps:

[0039] Step one, build a broad robust multi-user detection model. For multi-user detection in the discrete synchronous CDMA signal model, the received signal at a certain moment is the sum of K user signals and no...

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Abstract

The invention relates to a robust multi-user detection method based on a quantum Hopfield neural network and a quantum fish swarm algorithm in an impact noise environment. The method comprises the steps that a robust multi-user detection model is established; the quantum Hopfield neural network is activated, and a second-best solution is generated; a quantum fish swarm is initialized; an evolution rule of the quantum artificial fish swarm algorithm is used for carrying out evolution on a population; according to a food concentration function, food concentration values are computed for all new positions; and an obtained global optimum position is sending data for detecting a plurality of users, and detecting results are output. Robust multi-user detection in the strong impact noise environment is well achieved, the designed quantum Hopfield neural network and the quantum fish swarm algorithm are used as evolution strategies, and the designed method has the advantages of being high in convergence rate and high in convergence precision.

Description

technical field [0001] The invention relates to a robust multi-user detection method based on quantum Hopfield neural network and quantum fish swarm algorithm considering impact noise environment. Background technique [0002] CDMA system has developed rapidly in recent years and has become the mainstream technology of wireless communication system due to its unique advantages such as multi-channel composite access capability, anti-multipath fading ability, anti-narrowband interference ability and security / secrecy performance. Multiple access interference and near-far effect are key factors that affect the performance of CDMA systems and cannot be avoided. Multi-user detection technology based on information theory can reduce the impact of multiple access interference and near-far effect on system performance. In the environment of additive Gaussian noise, the intelligent multi-user detection technology can make the detection performance reach the theoretical optimal value, ...

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Application Information

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IPC IPC(8): H04B1/7105G06N3/00G06N3/02
Inventor 高洪元李晨琬徐从强齐研邵梦琦高璐
Owner HARBIN ENG UNIV
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