Samsung and KDDI Successfully Complete AI-Powered 5G Network Optimization Trial on Commercial 5G SA Network in Japan
Samsung Electronics has announced the successful completion of an extensive trial of its AI-powered RAN Speed ed Optimizer (RSO) solution on KDDI’s commercial 5G Standalone (SA) network in Japan, marking another milestone in the evolution of intelligent network management and automation. The initiative demonstrates how artificial intelligence can play a critical role in optimizing network performance, improving operational efficiency, and enhancing user experiences in next-generation mobile networks.
The collaborative trial between Samsung and KDDI began in late 2025 and continued over several months, focusing on evaluating the capabilities of AI-driven radio access network optimization in real-world commercial environments. By leveraging advanced machine learning technologies, the companies sought to demonstrate how AI can transform network operations, enabling mobile operators to respond more effectively to changing traffic demands while maximizing network resources.
The field trial was conducted across multiple locations in and around Tokyo, covering a broad range of deployment scenarios including densely populated urban districts, suburban neighborhoods, and rural regions. This diverse environment allowed engineers to assess the performance of Samsung’s AI-powered optimization technology under varying traffic patterns, user densities, and radio conditions. Such a wide-ranging deployment provided an ideal foundation for training and validating AI models designed to deliver highly adaptive network optimization.
The trial utilized 100 MHz of 3.7 GHz Time Division Duplex (TDD) spectrum distributed across hundreds of commercial 5G cells. By operating in live network conditions and analyzing large volumes of real-time data, Samsung’s AI algorithms were able to continuously learn and adapt to changing circumstances. The extensive dataset generated during the trial enabled more accurate modeling of network behavior and improved optimization outcomes across different geographic and usage scenarios.
Results from the trial highlighted the substantial impact AI can have on enhancing 5G network performance. During peak traffic periods, Samsung’s RAN Speed Optimizer achieved an average improvement of 31 percent in 5G downlink throughput across the overall test area. Performance gains were particularly impressive in high-density urban environments, where throughput improvements reached up to 52 percent.
These results demonstrate the potential of artificial intelligence to significantly increase network capacity and improve the overall quality of service delivered to subscribers. As mobile data consumption continues to grow and operators face increasing pressure to support bandwidth-intensive applications, AI-powered optimization solutions could become essential tools for maintaining high levels of network efficiency and user satisfaction.
Unlike conventional optimization techniques that often rely on static configurations or group-based parameter settings, Samsung’s AI-powered RAN Speed Optimizer introduces a more granular and intelligent approach. Traditional optimization methods typically apply identical parameter configurations across clusters of neighboring cells, limiting their ability to account for unique site characteristics and localized traffic conditions.
Samsung’s RSO technology addresses this limitation by providing cell-specific optimization capabilities. Using advanced AI-based prediction models, the system automatically analyzes site environments, traffic patterns, and network conditions to determine the most effective settings for each individual cell. This tailored optimization approach enables more precise resource allocation and allows operators to maximize network performance on a site-by-site basis.
At the core of the RAN Speed Optimizer is Samsung’s proprietary artificial intelligence engine, which continuously evaluates network performance indicators and predicts optimal parameter adjustments. The system can proactively respond to fluctuations in user demand, congestion levels, and environmental factors without requiring extensive manual intervention from network engineers.
By leveraging AI-driven automation, operators can reduce the complexity associated with traditional network tuning processes while improving responsiveness to changing conditions. This capability is becoming increasingly important as 5G deployments expand and network architectures become more sophisticated.
Samsung’s RSO solution is integrated within the Samsung CognitiV Network Operations Suite (NOS), a comprehensive portfolio of AI-powered network management technologies designed to support autonomous and intelligent telecommunications infrastructure.
The CognitiV NOS platform combines network automation tools, intelligent applications, and AI agents to streamline operations and improve decision-making capabilities across the network lifecycle. These technologies are intended to help operators manage growing traffic demands, optimize resource utilization, and accelerate the transition toward self-organizing and self-healing networks.
As operators continue their journey toward fully autonomous networks, solutions such as the RAN Speed Optimizer represent a significant advancement in operational intelligence. By minimizing the need for manual optimization processes, AI-driven systems can lower operating costs, reduce human error, and enable faster adaptation to emerging service requirements.
For mobile network operators, the benefits of AI-based optimization extend beyond performance improvements alone. The ability to automatically adjust network parameters according to real-time conditions can substantially reduce operational overhead and improve cost efficiency.
Network teams traditionally spend considerable time monitoring performance metrics, analyzing congestion patterns, and manually adjusting configurations to maintain service quality. AI-powered systems automate many of these tasks, allowing engineers to focus on higher-value strategic initiatives rather than repetitive optimization activities.
Furthermore, intelligent optimization solutions can support more sustainable network operations by improving energy efficiency and ensuring resources are allocated where they are most needed. As operators seek to balance rising demand with environmental considerations, AI technologies are expected to play an increasingly important role in enabling greener telecommunications infrastructure.
End users also stand to gain significantly from AI-enhanced network optimization. Improved throughput and more responsive network behavior translate into better experiences for consumers and businesses alike. Whether users are streaming high-definition video, participating in virtual meetings, browsing online content, or engaging with cloud-based applications, enhanced network performance can lead to smoother and more reliable connectivity.
The increasing adoption of immersive technologies, including augmented reality, virtual reality, cloud gaming, and advanced enterprise applications, places additional demands on 5G networks. AI-powered optimization solutions help ensure that these emerging services can be supported effectively, even during periods of high network utilization.
The successful collaboration between Samsung and KDDI demonstrates how artificial intelligence is becoming an integral component of future mobile networks. As 5G Standalone deployments continue to expand worldwide and preparations for 6G technologies accelerate, intelligent automation is expected to become a key differentiator for network operators seeking to deliver superior service experiences.
The trial serves as evidence that AI can provide measurable benefits in commercial network environments while paving the way for more adaptive, efficient, and resilient telecommunications infrastructure. By combining advanced machine learning capabilities with real-world operational expertise, Samsung and KDDI are helping shape the future of intelligent connectivity and setting new standards for network optimization in the era of AI-driven communications.
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