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A wcdma cell search method based on machine learning

A technology of machine learning and cell search, which is applied in the field of WCDMA communication, can solve the problems of complex cell search and failure, and achieve the effect of fast frame synchronization and flexible cell search

Active Publication Date: 2021-12-03
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The process of cell search is relatively complicated, and the secondary synchronization code pattern stipulated in the agreement must correspond to the primary scrambling code before processing. If the pattern table is changed or the secondary synchronization code is changed, subsequent processing cannot be performed.

Method used

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  • A wcdma cell search method based on machine learning
  • A wcdma cell search method based on machine learning
  • A wcdma cell search method based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] The specific process of the cell search of the embodiment is as follows: image 3 Shown:

[0042] 1) Network construction

[0043] Because the structure of the WCDMA downlink signal in each time slot is basically the same, a 38400 GOLD code truncated sequence is used to scramble a frame of signal, and the GOLD code itself is a pseudo-random sequence generated by certain rules. The way to train and learn the corresponding features, and the RNN network family is suitable for learning sequence data, and has excellent effects in the field of natural language processing and time series processing, so RNN neurons are used to build the network.

[0044] Network structure such as Figure 4 As shown, including the input layer InputLayer (Simple_rnn_1_input), the hidden layer SimpleRNN (Simple_rnn_1) built by the cyclic neural network layer, the fully connected layer Dense (Dense_1) and the output layer Activation (Activation_1); the data format is (batch size, number of channe...

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Abstract

The invention provides a WCDMA cell search method based on machine learning. In the training phase, a WCDMA downlink signal without a secondary synchronization code is generated to make a training data set, so as to get rid of the dependency between the primary scrambling code and the secondary synchronization code; test During the stage, on the basis of the time slot synchronized signal, according to the 15 positions that may appear in the frame header, the corresponding predicted data to be identified is produced to identify the main scrambling code, and the main scrambling code classification of the group of 15 predicted data to be identified is obtained Results and corresponding probabilities, and finally by searching the 15 maximum predicted probabilities, the position of the frame header and the main scrambling code used by the signal are obtained. The present invention does not need to know the secondary synchronization code and pattern table corresponding to the primary scrambling code, and achieves fast frame synchronization and classification and identification of 512 scrambling codes through machine learning, thereby completing cell search more flexibly.

Description

technical field [0001] The invention relates to WCDMA communication technology, in particular to WCDMA signal scrambling code identification technology. Background technique [0002] A WCDMA frame consists of 38400 chips, which are divided into 15 time slots, and each time slot contains 2560 chips. In the main synchronization process, the position of a time slot can be determined, and the next step is to determine the position of a radio frame header. The secondary synchronization process can determine the frame header, that is, the position of the 0th time slot and the number of the scrambling code group. There are 512 primary scrambling codes for the downlink, divided into 64 groups of 8. Each cell is assigned only one primary scrambling code. [0003] The structure design of the secondary synchronization channel S-SCH channel is as follows figure 1 shown. The structure of the S-SCH channel is very similar to that of the primary synchronization channel P-SCH. The firs...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04J11/00H04B7/26G06N20/00
CPCG06N20/00H04B7/2668H04J11/0069
Inventor 张雄潘晔邵怀宗林静然利强
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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