From 4G to 5G: Self-organized Network Management meets Machine Learning (1707.09300v2)

Abstract: In this paper, we provide an analysis of self-organized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.

• The paper demonstrates how ML empowers self-organized network management to automate configuration, optimization, and healing processes.
• It details the use of reinforcement, supervised, and unsupervised learning to enhance network performance and reliability.
• The study addresses challenges like data accessibility and computational complexity while outlining future research on deep learning and multi-agent systems.

Machine Learning: Enhancing Self-Organized Network Management from 4G to 5G

The advancement from 4G to 5G in wireless communications is significantly characterized by the application of self-organized network (SON) management, heavily leveraging ML techniques. This paper offers a comprehensive analysis of these methodologies across end-to-end network architectures, emphasizing the pivotal role of SON in automating operational tasks, thereby reducing costs and enhancing network performance.

Key Considerations and Strategies

The transition from hardware-centric advancements to software-driven improvements marks a distinct shift in 5G networks. The capabilities of SON span across self-configuration, self-optimization, and self-healing, crucial for maintaining operational efficiency in increasingly complex network environments. By deploying ML, these networks can adapt autonomously, learning and optimizing from historical data and performance metrics.

The major functions of SON include:

1. Self-Configuration: Automated configuration of network elements, significantly reducing manual interventions.
2. Self-Optimization: Continuous enhancement of network parameters driven by real-time data analysis, including load balancing and interference coordination through adaptive algorithms.
3. Self-Healing: Automated detection and compensation for network faults, minimizing the impact of failures on service quality.

Machine Learning in Network Management

Machine learning provides the underpinning for SON by enabling the network to learn from data, thus improving its decision-making capabilities. Various ML techniques are employed:

• Reinforcement Learning (RL): Used for dynamic adjustment of network parameters, learning optimal configurations over time through interactions with the network environment.
• Supervised Learning (SL): Applied in estimating network performance metrics and predicting potential faults by training on historical data.
• Unsupervised Learning (UL): Used for anomaly detection and clustering, helping in pattern identification and enhancing self-healing functionalities by spotting irregular behaviors.

Data Utilization

The effective application of ML in SON relies significantly on the comprehensive utilization of vast amounts of data generated by network operations. This data originates from control and management activities like Call Data Records (CDR), performance management reports, and MDT (Minimization of Drive Tests) data, offering insights into network performance and user experiences. By applying ML, this data can be transformed into actionable insights, leading to smarter, more efficient network management.

Challenges and Future Directions

Despite the advancements, the adoption of ML in network management is not without challenges. Access to real-time data for training ML models is often restricted due to privacy concerns or proprietary limitations. Addressing this requires collaboration between academia and industry to facilitate data sharing while ensuring privacy and security. Moreover, the integration of ML into network operations necessitates overcoming issues of computational complexity and ensuring the robustness and reliability of decisions made by learning algorithms.

Future research should explore:

• Enhanced Data Accessibility: Developing frameworks for secure, privacy-preserving access to real-world network data.
• Deep Learning: Exploring deep learning frameworks for more complex scenarios encountered in 5G, leveraging big data analytics.
• Autonomous Network Management: Expanding the vision of distributed, multi-agent learning systems for full-spectrum network management across the diverse 5G ecosystem.

In conclusion, the implementation of ML techniques in SON for 4G and beyond to 5G represents a transformative step in network management. By reducing human intervention and enhancing automation, these systems promise more efficient, adaptable, and resilient telecommunications infrastructures. Addressing existing challenges will pave the way for further innovations, ensuring that network functions not only meet current demands but are poised for future challenges.