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Intelligent prediction method of mobile communication system based on improved convolutional neural network

A mobile communication system, convolutional neural network technology, applied in the field of mobile communication to achieve good performance

Active Publication Date: 2021-06-29
QINGDAO UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The wireless mobile communication environment has become increasingly complex, diverse and dynamic, which has brought great challenges to intelligent mobile communication

Method used

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  • Intelligent prediction method of mobile communication system based on improved convolutional neural network
  • Intelligent prediction method of mobile communication system based on improved convolutional neural network
  • Intelligent prediction method of mobile communication system based on improved convolutional neural network

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Embodiment 1

[0053] A mobile communication system intelligent prediction method based on improved convolutional neural network, including the following parts

[0054] S1: Establish a mobile collaborative communication system model, select 2-Nakagami communication channel;

[0055] Such as figure 1 As shown, first establish a mobile collaboration communication system model, a mobile source (MS), multiple mobile relays (MR), and a mobile destination (MD), together form a mobile collaboration communication system, communication channel pick 2- Nakagami channel; MS has n t Root launch antenna, MD has n r Root receiving antenna, MR uses 1 antenna.

[0056] In order to represent the channel gain of MS → MR, MS → MD, MR → MD 3 links, we define variable h = h g , g∈ {SR, SD, RD}. We use g SR , G SD , G RD The location gain of MS → MR, MS → MD, MR → MD link, respectively .G SD = 1.

[0057] The total amount of emission of MS and MR is e, k is the power distribution coefficient. The first Root launch an...

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Abstract

A mobile communication system intelligent prediction method based on an improved convolutional neural network. First, a mobile cooperative communication system model is established, and a 2-Nakagami communication channel is selected; for the transmitting antenna selection scheme, a closed expression of the OP performance of the communication system is derived; Select the CNN model, remove the pooling layer in the model, input the channel parameters that affect the OP performance of the communication system as the model input, and then train through the training samples, that is, the intelligent prediction of the mobile communication system based on the improved convolutional neural network is obtained Model. Compared with the existing algorithm, the prediction method provided by the invention has better performance, which verifies the feasibility and effectiveness of the invention.

Description

Technical field [0001] The present invention belongs to the technical field of mobile communication, and more particularly to a mobile communication system intelligent prediction method based on improved convolutional neural network. Background technique [0002] In recent years, with the explosive growth of users, the fifth generation mobile communication technology has received extensive attention. With the rapid development of 5G mobile technology, there are continuous emergence of all kinds of new equipment and application scenarios. The type of business is also growing. surroundings. The current mobile communications field is facing intelligent, broadband, diversified, and many technical challenges. Therefore, complex and multi-change mobile communication environments make users put forward higher communication quality requirements for mobile communications, such as large capacity, high-speed mobile access, and ultra-low delay, etc., these are key issues facing 5G mobile com...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W24/06H04W16/22G06N3/04G06N3/08
CPCH04W24/06H04W16/22G06N3/08G06N3/045
Inventor 徐凌伟权天祺张威龙周新鹏王涵李辉陶冶
Owner QINGDAO UNIV OF SCI & TECH
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