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Centralized resource management method based on genetic algorithm

A genetic algorithm and resource management technology, applied in the field of wireless communication, can solve problems such as limiting system performance, not considering user business needs, and not meeting the status quo of mobile communication services

Active Publication Date: 2013-10-09
BEIJING UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This inflexible power allocation can limit system performance
In addition, the solutions proposed in the above documents do not consider the different business needs of users, which undoubtedly does not meet the current business status of mobile communications.

Method used

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  • Centralized resource management method based on genetic algorithm
  • Centralized resource management method based on genetic algorithm
  • Centralized resource management method based on genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0064] like figure 1 As shown, this embodiment describes a centralized resource management method based on a genetic algorithm, and the method includes the following steps:

[0065] S1. Integrate network resources in the system and users in the system, perform two-dimensional chromosome coding for resource allocation, and randomly generate N individuals as the initial population, where N is an integer greater than 2;

[0066] S2. Perform dynamic power allocation for each chromosome, and construct an individual fitness function based on the power allocation and user requirements;

[0067] S3. Carry out population reproduction, including: selection, crossover, mutation and revision process, to maintain the same number of offspring individuals as the number of parent individuals;

[0068] S4. Replace the parent with the child, and repeat the population reproduction process until the iteration termination condition is met;

[0069] The network resources refer to other network re...

Embodiment 2

[0153] This embodiment further provides the matlab simulation experiment and results of the first embodiment. Set the experimental value of the parameters of Example 1: set the number of users in the system K=10, where the number of real-time users is K 1 =4, the number of non-real-time users K 2 =6; the number of antennas is I=7, which are randomly distributed in the system; the number of sub-channels is M=20. Each user has at most N a = 3 antenna services, occupying at most N s =4 subchannels. The channel response between the base station and the user considers large-scale fading, shadow fading, and Rayleigh fading. For the genetic algorithm, each generation of the population contains N p =50 individuals, a total of N g = 100 generations of search, the iteration is terminated, and the mutation probability is p m =0.05.

[0154] For the user's utility function, let C in the real-time user's utility function 1 = 2, C in the utility function for non-real-time users 2 ...

comparative approach 1

[0157] Comparison scheme 1: The antennas are allocated according to the path loss, and the sub-channels are randomly allocated

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Abstract

The invention discloses a centralized resource management method based on a genetic algorithm, and relates to the field of wireless communication. The method mainly includes the steps that S1, network resources and users in a system are integrated, two-dimension chromosome coding is carried out on resource allocation, and N individuals are generated randomly and serve as an initial population, wherein N is an integer larger than two; S2, dynamic power distribution is carried out on each chromosome, and based on the power distribution and user requirements, fitness functions of the individuals are built; S3, population propagation is carried out, wherein the population propagation includes the processes of selection, intersection, mutation and correction, and the number of filial generation individuals is kept to be identical to that of parent individuals; S4, the parent individuals are replaced by the filial generation individuals, and the population propagation processes are repeated until an iteration stopping condition is met. The use ration of the power of the system can be improved, under the condition that the real-time user requirements are met, fairness among non-real-time users can still be effectively ensured, and system performance is greatly improved.

Description

technical field [0001] The present invention relates to the technical field of wireless communication, in particular to a centralized resource management method based on a genetic algorithm. Background technique [0002] In recent years, with the development of mobile communication technology, the support capability of mobile communication systems for wireless communication services has been significantly improved. However, users also have higher demands for high-speed, high-quality multimedia services. Therefore, in the research of next-generation mobile communication technology, higher requirements are put forward for spectrum efficiency, transmission rate, system throughput and cell edge performance. [0003] Cooperative multipoint transmission technology has become one of the research hotspots in recent years because it can effectively improve the performance of cell edge users and reduce or even eliminate inter-cell interference. Its core idea is to extend the traditi...

Claims

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Application Information

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IPC IPC(8): H04W72/04H04W52/24
Inventor 许晓东戴巡张慧鑫陶小峰张平
Owner BEIJING UNIV OF POSTS & TELECOMM
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