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Application of improved Naive Bayes classification algorithm in 5G signaling analysis

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Abstract

Due to the rapid development of the wireless communication technology, the data volume of 5G mobile network continues to grow, which leads to the continuous reduction of signaling analysis and processing efficiency. To overcome the problem, the intelligent communication will be one of the mainstream directions of mobile communication development which combines with 5G and AI. In this paper, we introduce a method of using machine learning classification and signaling analysis technology. The proposed method in this paper is an improved signaling analysis algorithm based on naive Bayesian classification, which improves the signaling classification accuracy of the algorithm. In the signaling analysis process of the algorithm, the key messages of signaling data are selected as user characteristics before the association and synthesis of user signaling processes. Then, the supervised signaling feature classification model is trained according to the user ID in the signaling data, and the model is used for signaling classification. Subsequently, aiming at the zero probability problem in the algorithm, we use Laplace smoothing to correct it. Then, the algorithm continues to use orthogonal matrix to make orthogonal transformation on continuous attributes and discrete attributes after numerical marking, so as to enhance the independence between attributes. It closes to the assumption of naive Bayes, which improves the classification accuracy of the algorithm. The experimental results show that the improved algorithm model has high comprehensive performance index and good classification performance. The F1 score of this algorithm reaches 67.86%, and achieves the expected effect. The improved signaling analysis method proposed in this study is expected to be useful in 5G network optimization and testing.

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Data availability

In the paper, this dataset is the cellphone CDR data of the Chinese city Shenzhen for 1 Day. This dataset is for academic research only. All rights reserved. For privacy concerns, all specific date info was removed and all identifiable IDs have been replaced by serial numbers in each kind of data.

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Funding

This work is supported by the National Key R&D Program of China (2021YFB2700300); Chongqing Education Commission Science and technology research project (KJQN201902402).

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Authors and Affiliations

  1. Chongqing Vocational College of Transportation, Chongqing, 402247, China

    Wanwan Wang & Zhenghong Jiang

  2. Chongqing City Management College, Chongqing, 401520, China

    Yu Duan

  3. Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Longhan Cao

Authors
  1. Wanwan Wang
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  2. Yu Duan
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  3. Longhan Cao
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  4. Zhenghong Jiang
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Correspondence to Yu Duan.

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Wang, W., Duan, Y., Cao, L. et al. Application of improved Naive Bayes classification algorithm in 5G signaling analysis. J Supercomput 79, 6941–6964 (2023). https://doi.org/10.1007/s11227-022-04946-x

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