Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping

Gupta, Akanksha; Ghanshala, Kamal Kumar; Joshi, Rohit

doi:10.9781/ijimai.2021.03.004

Cited by 11 publications

(5 citation statements)

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“…It might be either cate-gorizing data (classification problem) or forecasting an outcome (regression algorithms). Predicting the coverage of 5G mobile networks also can be categorized as classification type problem [26]. Classification is a type of supervised machine learning in which the model attempts to predict the proper label of given input data.…”

Section: Classification Algorithms In Machine Learning For 5g Coverag...mentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

Yuliana,

Iskandar,

Hendrawan

2024

IEEE Access

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5G technology is a key factor in delivering faster and more reliable wireless connectivity. One crucial aspect in 5G network planning is coverage prediction, which enables network providers to optimize infrastructure deployment and deliver high-quality services to customers. This study conducts a comprehensive analysis of machine learning algorithms for 5G coverage prediction, focusing on dominant feature parameters and accuracy. Notably, the Random Forest algorithm demonstrates superior performance with an RMSE of 1.14 dB, MAE of 0.12, and R2 of 0.97. The CNN model, the standout among deep learning algorithms, achieves an RMSE of 0.289, MAE of 0.289, and R2 of 0.78, showcasing high accuracy in 5G coverage prediction. Random Forest models exhibit near-perfect metrics with 98.4% accuracy, precision, recall, and F1-score. Although CNN outperforms other deep learning models, it slightly trails Random Forest in performance. The research highlights that the final Random Forest and CNN models outperform other models and surpass those developed in previous studies. Notably, 2D Distance Tx Rx emerges as the most dominant feature parameter across all algorithms, significantly influencing 5G coverage prediction. The inclusion of horizontal and vertical distances further improves prediction results, surpassing previous studies. The study underscores the relevance of machine learning and deep learning algorithms in predicting 5G coverage and recommends their use in network development and optimization. In conclusion, while the Random Forest algorithm stands out as the optimal choice for 5G coverage prediction, deep learning algorithms, particularly CNN, offer viable alternatives, especially for spatial data derived from satellite images. These accurate predictions facilitate efficient resource allocation by network providers, ensuring high-quality services in the rapidly evolving landscape of 5G technology. A profound understanding of coverage prediction remains pivotal for successful network planning and reliable service provision in the 5G era.

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Section: Classification Algorithms In Machine Learning For 5g Coverag...mentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

Yuliana,

Iskandar,

Hendrawan

2024

IEEE Access

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“…Each entry is accompanied by authoritative citations, thus serving as both a summary and a guide for further reading. For example, Gupta et al [147] have contributed to Linear Regression, while Ali et al [101] have worked on the intricacies of deep neural networks within a 5G context. The unsupervised learning section of the table is similarly exhaustive, covering methodologies ranging from k-means clustering to Affinity Propagation Clustering.…”

Section: Security Protocol Optimization With Machine Learningmentioning

confidence: 99%

A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions

Fakhouri,

Alawadi,

Awaysheh

et al. 2023

Electronics

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Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms’ role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network’s security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network’s underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security.

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“…Bagging is a technique to decrease the variance in the prediction by generating subsets of data chosen randomly with replacement from the original training dataset [26]. Meanwhiles, Boosting is an iterative technique that adjusts the weight of an observation based on the prior learner's result [15]. ET can overcome the overfitting issues in RT [35], [45], which is more robust to the noise.…”

Section: E Ensembles Of Trees (Et)mentioning

confidence: 99%

“…GPR is a Bayesian non-parametric model [15] that utilizes kernel functions in solving regression problems [47]. It derives the relationship between input parameters and response variables from unknown functions [28].…”

Section: F Gaussian Process Regression (Gpr)mentioning

confidence: 99%

“…Therefore, 5G network development requires extensive and critical radio network planning and analysis. It will involve numerous types of data and factors to identify the best positions for antenna deployment [15]. Because of the complexity of planning and analysis works which are not feasible to be executed using traditional methods, a high-level data analytics adoption using machine learning (ML) is required [5], [9].…”

Section: Introductionmentioning

confidence: 99%

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Mobile Network Coverage Prediction Based on Supervised Machine Learning Algorithms

et al. 2022

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The need for wider coverage and high-performance quality of mobile networks is critical due to the maturity of Internet penetration in today's society. One of the primary drivers of this demand is the dramatic shift toward digitalization due to the Covid-19 pandemic impact. Meanwhile, the emergence of the 5G wireless standard and the increasingly complex actual operating environment of mobile networks make the traditional prediction model less reliable. With the recent advancements and promising capabilities of machine learning (ML), it is seen as an alternative to the traditional approaches for ground to ground (G2G) mobile communication coverage prediction. In this study, various ML models have been tested and evaluated to develop an ML-based received signal strength prediction model for mobile networks. However, the challenge is to identify a practical ML model that can fulfill the computing speed criteria while still meeting the prediction accuracy. A total of six categories of ML models, namely Linear Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Regression Trees (RT), Ensembles of Trees (ET), and Gaussian Process Regression (GPR) that consists of more than 20 types of established algorithms/kernels have been tested and evaluated in this paper to identify the best contender among them, in terms of speed and accuracy. Findings from the evaluation showed that the GPR model is the most accurate model for Reference Signal Received Power (RSRP) prediction in terms of RMSE and R 2 , followed by ET, RT, SVM, ANN and LR. Nevertheless, prediction speed and model training times are also important factors in determining the most practical model for RSRP prediction for several real-world mobile network planning applications. Finally, the ET model with Random Forest (RF) algorithm has been selected and highly recommended as the most practically employed ML model for developing rigorous RSRP predictions model in multi-frequency bands and multi-environment. The developed prediction model is capable of being utilized for the network analysis and optimization.

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Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping

Cited by 11 publications

References 0 publications

Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions

Mobile Network Coverage Prediction Based on Supervised Machine Learning Algorithms

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