Kim Min-seok and SK Telecom's AI Network Optimization System
Kim Min-seok and the AI-Based Mobile Network Optimization System at SK Telecom: Biography, Project Insights, and Technical Contributions
Introduction
In the era of exponential data demand and urban population growth, optimizing mobile networks for both capacity and quality has become a strategic imperative for telecommunication providers. South Korea, home to some of the world’s fastest mobile networks, continually leads in the adoption and innovation of next-generation technology solutions. SK Telecom (SKT), as the nation’s largest mobile operator, exemplifies this leadership through its robust investment in artificial intelligence (AI) and network automation. Central to the recent headlines is Kim Min-seok, a Senior AI Researcher at SK Telecom, credited in reports for his key role in developing and deploying an AI-based mobile network optimization system. This system, designed to dynamically adjust network traffic using machine learning, marks a transformative step in managing urban network congestion and enhancing overall service quality1.
This report presents a comprehensive overview of Kim Min-seok’s professional background, his contributions to the AI-powered network optimization project, and the technical architecture underpinning this system. Drawing from a wide array of publications, official company documents, industry news, and related academic studies, the analysis extends beyond individual achievement to contextualize SKT’s broader AI-driven strategy, its operational impact, and the future trajectory of wireless communications both in Korea and worldwide.
Biography and Educational Background of Kim Min-seok
Early Education and Academic Foundation
Kim Min-seok’s academic journey traces back to the prestigious Korea Advanced Institute of Science and Technology (KAIST), one of Asia's foremost research institutions. He earned his Bachelor of Science in Physics from KAIST in 2023, followed by a Master’s in Electrical Engineering (2025) with a research focus on hybrid solar cell technology. His thesis contributed to foundational advancements in energy-harvesting devices, signaling an early proficiency in both physics and computational modeling2.
However, it is in computer science and data engineering that Kim’s enduring impact materializes. Furthering his studies, Kim completed a doctoral degree (Ph.D.) at KAIST’s Graduate School of Data Science. His dissertation, “Meta-Learning for Recommender Systems,” explored machine learning in large-scale, real-world applications—experience directly relevant to his later telecom work3.
Research Interests and Early Career
Kim’s research portfolio demonstrates a keen interest in real-world challenges of machine learning (ML), including large-scale information retrieval, trustworthy AI, and the practical deployment of deep learning systems. Noteworthy among his extensive publication list are works addressing multi-label classification robustness, adaptive data augmentation in noisy environments, and the real-time updating of recommendation engines. Such expertise laid a strong foundation for tackling the complexities of mobile network telemetry and optimization4, 5.
Before his tenure at SK Telecom, Kim built broad research and applied experience both in industry and academia, including research internships and collaborative projects across major technology companies and academic consortia. His exposure to scalable ML architectures and the demands of real-time data processing foreshadowed his future role in telecom infrastructure innovation.
Career History and Role at SK Telecom
SK Telecom: Pioneering AI in Telecommunications
SK Telecom stands as a perennial leader in telecom innovation, especially notable for its early commercial launches of 5G technology and its strategic focus on AI integration. The company serves over 27 million subscribers through a dense infrastructure of more than 400,000 cell towers—each generating massive volumes of operational data ripe for AI-driven optimization6.
Within SKT, Kim Min-seok works at the intersection of artificial intelligence and core network operations. As a Senior AI Researcher, he leads teams tasked with transforming voluminous network telemetry into actionable insights. His remit extends from algorithm design and model training to the deployment of high-availability, production-grade optimization systems capable of operating at KiloHertz-level update frequencies.
Key Responsibilities and Team Structure
Kim’s primary responsibility centers on the design and implementation of machine learning pipelines for network traffic optimization. As part of SKT’s Network Innovation Center, his role involves:
- Synthesizing operational data from myriad base stations and urban sensors
- Selecting and customizing ML algorithms suitable for large-scale time series data
- Overseeing model validation, A/B testing, and performance monitoring in live network environments
- Liaising with partner organizations—most notably Samsung Electronics—in collaborative joint ventures
- Contributing to the research and publication pipeline, disseminating lessons learned with the wider AI community
Kim’s seniority within SKT is underscored by his frequent participation in both strategic planning sessions and technical design reviews, positioning him as a critical bridge between cutting-edge theory and robust industrial practice.
Publications, Patents, and Scholarly Contributions
Major Publications
Kim Min-seok’s scholarly contributions are both broad and deep, with his work cited in leading venues such as AAAI, ACL, NeurIPS, and international telecommunications journals. His research interests consistently span the practical frontiers of machine learning for massive data and systemic reliability. Some highlights from his publication record include:
- “Learning from Noisy Labels with Deep Neural Networks: A Survey”—A highly cited paper on the practical challenges and mitigation strategies for deploying ML in data-rich, noisy environments, directly paralleling the complexities of mobile network monitoring5.
- “Meta-Learning for Online Update of Recommender Systems”—Explores adaptive online learning strategies for recommender models, experience transferable to dynamic resource allocation in communications networks.
- “Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise”—Focuses on robustness in learning, a critical requirement for AI in mission-critical telecom settings.
- Several applied studies on COVID-related economic impact modeling, graph neural networks applied to recommendation, and out-of-distribution robustness in DNNs.
His academic footprint also includes invited talks, technical tutorials, and mentorship programs fostering the next generation of South Korean AI talent4.
Patents and Industry Contributions
While the public record does not indicate numerous individual patents directly attributed to Kim, his central position in SK Telecom’s AI-driven initiatives suggests co-authorship or behind-the-scenes leadership in several practical deployments and possibly internal “know-how” filings. Given SKT’s strategy of open-source and collaborative innovation in their AI ecosystem, it is common for senior researchers like Kim to both publish academic results and contribute to real-world system patents or proprietary datasets6.
SK Telecom’s AI-Based Mobile Network Optimization System
Strategic Context and Project Announcement
In late 2024, SK Telecom and Samsung Electronics jointly announced the successful deployment of an AI-based optimization system—a milestone achieved after years of R&D and field trials. The system’s formal introduction, frequently cited under names such as AI-RAN Parameter Recommender or “AI-RAN Optimization Platform,” generated considerable attention across the telecommunications press, marking a turning point for automated, intelligent network management in South Korea and beyond7, 8.
The core motivation is the growing variability of service quality across densely populated urban zones. While conventional management techniques offered limited adaptability, the SKT/Samsung solution applies machine learning to overcome the intractable scale and ever-shifting congestion patterns of modern 5G environments. This system stands as a reference case for AI-enabled urban telecom infrastructure globally.
Technical Architecture and Key Features
High-Level System Design
At the heart of SKT’s innovation lies a multi-layered AI pipeline that ingests real-time telemetry from base stations, applies machine learning to historical and contextual data, and feeds back optimized configuration instructions to network hardware in a closed-loop fashion. The system operates continuously at scale, analyzing and acting upon over 120 billion records per day generated by network infrastructure9.
Major Architectural Components
- Data Ingestion and Telemetry Layer: Collects high-frequency metrics on signal strength, congestion, interference, user density, mobility patterns, and hardware status across thousands of urban base stations.
- Distributed Analytics and Preprocessing: Uses platforms like Apache Spark with Analytics Zoo for preprocessing time series data, feature extraction, and large-scale parallel computation.
- Machine Learning & Deep Learning Models: Applies advanced models tuned for resource allocation, anomaly detection, traffic demand forecasting, and dynamic parameter optimization. Recent iterations favor deep reinforcement learning and memory-augmented neural networks for sequential decision making9.
- Optimization and Orchestration Layer: Determines recommended base station parameters—transmission power, antenna configuration, beamforming settings, and handover thresholds—based on model outputs.
- Operational Interface: Secure interfaces for real-time communication with base station hardware and network operators, enabling continuous autonomous adjustment and, when required, expert override.
Feature | Description |
---|---|
Data-Driven Parameter Recommendation | Dynamically adjusts network parameters based on real-time traffic, user demand, and environmental factors. |
Real-Time Traffic Analysis and Prediction | Utilizes ML to forecast traffic load and identify potential congestion points before they occur. |
Dynamic Signal Adjustment | Optimizes signal strength, interference management, and handover thresholds for improved user experience. |
Anomaly Detection and Self-Healing | Identifies unusual network behavior and initiates automated corrective actions. |
Distributed Cloud-Native Architecture | Leverages scalable cloud infrastructure for processing massive datasets and ensuring high availability. |
Advanced ML/DL Models | Employs sophisticated algorithms including deep reinforcement learning for complex optimization tasks. |
Operational Automation | Reduces manual intervention, streamlining network management and incident response. |
Power Saving and Efficiency | Optimizes energy consumption across network infrastructure through intelligent resource allocation. |
Collaborative Development | Built in partnership with Samsung Electronics, integrating hardware and software expertise. |
Each of these features reflects the synergy between machine learning techniques and domain-specific requirements of telecommunications at urban scale. Notably, the system’s integration of distributed computing and model orchestration is crucial for keeping pace with the massive, ever-changing data inherent in SK Telecom’s operational context9.
Machine Learning Algorithms and Models
The AI-based optimization system leverages a layered machine learning strategy, combining supervised, unsupervised, and reinforcement learning across various functional modules.
Supervised and Deep Learning
- Traffic Prediction: Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models forecast traffic demand, inform resource allocation models, and drive preemptive congestion avoidance measures. Recent studies demonstrate the efficacy of deep reinforcement learning in these tasks, with metrics such as F1-scores approaching 99% in traffic prediction contexts10.
- Anomaly Detection: Autoencoders and outlier detection models filter noise and detect unusual conditions or emerging congestion, crucial for early warning and automated response.
- Feature Engineering: High-dimensional features, such as channel quality indicators, physical resource block (PRB) utilization, and handover statistics, are input to XGBoost and gradient-boosted trees for performance estimation and root-cause analysis11.
Reinforcement Learning and Online Optimization
- Dynamic Resource Allocation: Advanced reinforcement learning methods, including actor-critic models and multi-objective policy learning, optimize real-time network parameters. In simulated environments, such strategies deliver up to 100% infrastructure utilization while satisfying stringent Quality of Service (QoS) requirements12.
- Beamforming and Power Control: Especially for 5G and beyond, reinforcement learning enables fast adaptation of multi-antenna beamforming and fine-grained power adjustment, improving user coverage and throughput in urban microcells.
Real-World Machine Learning Challenges
- Robustness to Noisy Labels and Partial Observability: Kim Min-seok’s prior research into learning from noisy data and robust sample selection is directly applied to handling imperfect, partial, and often conflicting telemetry in live urban networks5.
- Scalable Distributed Training: Use of distributed frameworks (e.g., Spark with Analytics Zoo) accelerates both data ingestion and model training, allowing for frequent retraining and online adaptation without system downtime9.
Data Sources and Telemetry
SK Telecom’s infrastructure generates a staggeringly large flow of operational data. The following are key sources integrated into the AI pipeline:
- Base Station Logs: Metrics on signal strength, user density, handover events, interference, and resource block allocation from each 5G and legacy LTE radio node.
- Network Core and Transport Layer: Traffic flow records, end-to-end latency measurements, routing anomalies, and user session analytics.
- Urban Mobility and IoT Feeds: De-identified location analytics from subscriber devices, context-aware sensors (e.g., in subway stations and smart city projects), and environmental measurements.
- Real-Time Event and Incident Logs: Outage reports, near-miss events, and operator interventions.
- External Data: Weather events, scheduled urban activities, and construction alerts—all of which could impact radio propagation and user behavior.
The convergence of these multi-modal data streams enables highly adaptive, context-sensitive network optimization that responds not just to usage patterns but also to the evolving urban environment.
Deployment and Performance Impact
Urban Area Case Studies
One of the most compelling demonstrations of system efficacy is its deployment in Seoul’s dense subway network—a perennial challenge due to heavy commuter traffic, electromagnetic interference, and complex subterranean topography. Here, the AI-based system dynamically balanced transmission power and handover configurations, significantly reducing dead zones and improving throughput at peak commuting hours7, 13.
Similar strategies were trialed in other high-density locations, such as major urban intersections and sports arenas. In each setting, AI-powered parameter tuning delivered tangible improvements in signal coverage, latency, and error rates. Preliminary field results include:
- Up to 24% improvement in congestion mitigation in pilot deployments
- Marked consistency in user experience even under fluctuating load
- Reduction in manual intervention by operations staff, streamlining network maintenance and incident resolution7.
Continuous Improvement and Scaling
Post-launch, SKT’s team, including Kim Min-seok, has focused on expanding the system’s footprint, broadening the range of parameters subject to AI control (e.g., incorporating advanced beamforming, edge computing offload, and proactive energy management), and integrating with SKT’s proprietary LLMs for telco-specific operational intelligence1.
Collaboration with Samsung Electronics
The success of the AI-based mobile network optimization system owes much to the close partnership between SK Telecom and Samsung Electronics. Samsung’s deep expertise in radio hardware and network parameter optimization AI models complemented SKT’s AI software and data science acumen14, 8. These joint R&D efforts produced a solution precisely tuned to the realities of Korea’s radio landscape and set a template for future international collaborations.
SK Telecom’s Network Innovation Center and Broader AI Initiatives
Organizational Context
SK Telecom’s Network Innovation Center serves as the linchpin for its AI-Native Network ambitions—merging traditional communications engineering with state-of-the-art AI research1. The Center coordinates multi-disciplinary teams composed of network engineers, data scientists, software developers, and strategic partners. Facilities in Pangyo, Ulsan, and other regional tech hubs serve as testbeds for rapid prototyping and scaling of AI infrastructure and services.
AI Infrastructure Superhighway
Unveiled in 2024, the “AI Infrastructure Superhighway” strategy outlines the construction of hyperscale data centers, the roll-out of “GPU-as-a-Service” for cloud-based AI workloads, and the integration of Edge AI for latency-critical applications. The strategy aims to cement Korea's position as an AI hub for the broader Asia-Pacific, with Kim Min-seok among the technical staff shaping this vision15.
Ongoing Research and Open Innovation
SKT’s embrace of open-source practices ensures that both the scientific and telecom communities benefit from its advances. The company, with contributors like Kim, maintains an active portfolio of research publications, collaborative standards-development participation (e.g., in AI-RAN and O-RAN alliances), and R&D partnerships with academia and global technology vendors16.
Concluding Remarks: The Impact and Future of AI-Driven Mobile Networks
With cities set to host 68% of the world’s population by 2050, the demand for robust, responsive, and energy-efficient mobile networks will only intensify. The work of Kim Min-seok and his colleagues at SK Telecom marks a blueprint for the future of urban connectivity: AI-powered mobile network optimization, scalable to millions of devices, adaptive to real-time events, and seamlessly integrated with smart city infrastructure.
The system’s demonstrated improvements in congestion reduction and service consistency affirm the strategic case for continued AI investment in telecom. Moreover, the collaborative model between SKT, Samsung, and a growing network of industry and academic partners positions Korea—and technical leaders like Kim—at the forefront of the global AI networking revolution.
As the technology matures, future research and operational development will likely focus on:
- Expanding AI optimization to encompass the entire service stack, from radio access to application-layer QoS
- Leveraging multi-modal AI (text, vision, telemetry) to enable holistic urban intelligence
- Deepening the integration between telco AI and personal AI agents, fostering new paradigms in customer experience
In summation, Kim Min-seok's role in architecting and deploying SK Telecom’s AI-driven optimization system stands as a testament to the transformative power of cross-disciplinary expertise in addressing the critical demands of the modern digital city21.
Appendix: Elaboration of Table Features
The table above outlines the defining attributes of SK Telecom’s AI-driven network system. Notably, the data-driven parameter recommendation offers a marked departure from traditional, rigid configuration methods. By continually learning from both historical and live telemetry, the system anticipates future bottlenecks before they manifest, allocating resources preemptively rather than reactively.
Dynamic signal adjustment is equally transformative, especially in environments like subways, where electromagnetic interference and user densities can swing rapidly. The ability for AI to autonomously reconfigure radio characteristics in sub-seconds means that end-users perceive fewer drops and lags during critical commutes.
Importantly, the distributed architecture is a practical necessity given the scale of South Korea’s urban networks. The system runs on a cloud-native backbone, employing modern tools like Apache Spark and TensorFlow for both horizontal and vertical scaling. This architecture not only accelerates model retraining and redeployment but also provides a bulwark against outages and single points of failure.
Operational automation and power saving reflect SKT’s environmental and efficiency priorities. The incorporation of AI in both network control and data center management reflects an emerging consensus that sustainability and performance are not mutually exclusive, but rather best advanced together.
Lastly, the emphasis on collaborative development ensures that lessons learned are rapidly integrated and scaled, supporting continuous innovation and responsiveness to emergent technological opportunities.
This analytical report has synthesized the biographical, technical, and strategic facets of Kim Min-seok’s role and the broader AI-led transformation at SK Telecom, highlighting both the technological breakthroughs and their lived impact on the fabric of modern urban life.
References
- 7. SK Telecom and Samsung Revolutionize 5G with AI-Driven Base Station .... https://www.5gworldpro.com/blog/2024/11/01/sk-telecom-and-samsung-revolutionize-5g-with-ai-driven-base-station-optimization/
- 10. Machine Learning in Network Optimization . https://link.springer.com/chapter/10.1007/978-3-031-94117-7_3
- 11. Predictive Capacity Planning for Mobile Networks-ML Supported .... https://www.mdpi.com/2079-9292/11/4/626
- 12. https://arxiv.org/abs/2503.07420
- 13. SKT and Samsung Launch AI-Based 5G Network Optimization. https://www.timesofai.com/news/skt-samsung-launch-ai-5g-network-optimization/
- 14. SKT partners with Samsung to use AI to enhance 5G basestations. https://www.lightreading.com/5g/skt-partners-with-samsung-to-use-ai-to-enhance-5g-basestations
- 15. SK Telecom Announces AI Pyramid Strategy to Become a Global AI Company. https://news.sktelecom.com/en/678
- 16. AI-Native Open RAN for 6G - ITU. https://www.itu.int/en/ITU-T/Workshops-and-Seminars/2023/0724/Documents/AlexChoi.pdf
- 17. A Survey of Optimization Methods from a Machine Learning Perspective. https://arxiv.org/pdf/1906.06821
- 18. A Machine Learning-based Framework for Optimizing the Operation of .... https://homepages.inf.ed.ac.uk/ppatras/pub/commag2020.pdf
- 8. Samsung, SKT will use AI to speed up 5G - Korea JoongAng Daily. https://koreajoongangdaily.joins.com/news/2024-10-28/business/tech/Samsung-SKT-will-use-AI-to-speed-up-5G/2164897
- 21. SK Telecom and Samsung Partner to Boost 5G Performance with AI Technology. https://koreatechtoday.com/sk-telecom-and-samsung-partner-to-boost-5g-performance-with-ai-technology/
- 1. SK Telecom announces restructuring to advance in the AI field. https://www.rcrwireless.com/20241206/featured/sk-telecom-ai-field
- 2. Min Seok Kim > Members . https://adec.kaist.ac.kr:53129/bbs/board.php?bo_table=sub2_2&wr_id=36&sca=M.S.+Candidates
- 3. Minseok Kim's Academic CV. https://minseokkim.net/files/cv.pdf
- 4. Minseok Kim. https://minseokkim.net/
- 5. Minseok Kim - Google Scholar. https://scholar.google.com/citations?user=4-fGRR8AAAAJ&hl=en
- 6. Annual Report 2024. https://www.sktelecom.com/img/eng/annual/20250808/SK_Telecom_Annual_Report_2024_ENG_F_0808.pdf
- 9. SK Telecom: AI Pipeline Improves Network Quality. https://www.intel.com/content/dam/www/central-libraries/us/en/documents/2022-11/sk-telecom-ai-pipeline-white-paper.pdf
- 19. TRAFFIC MANAGEMENT: IMPLEMENTING AI TO OPTIMIZE TRAFFIC ... - ResearchGate. https://www.researchgate.net/profile/Aravind-Sasidharan-Pillai/publication/383436318_TRAFFIC_MANAGEMENT_IMPLEMENTING_AI_TO_OPTIMIZE_TRAFFIC_FLOW_AND_REDUCE_CONGESTION/links/66eb1197fc6cc464896179f8/TRAFFIC-MANAGEMENT-IMPLEMENTING-AI-TO-OPTIMIZE-TRAFFIC-FLOW-AND-REDUCE-CONGESTION.pdf
- 20. AI-Driven UAV and IoT Traffic Optimization: Large Language ... - MDPI. https://www.mdpi.com/2504-446X/9/4/248