TOKYO, Japan, May 25, 2026 - (JCN Newswire via SeaPRwire.com) - NTT DOCOMO, INC. (“DOCOMO”) and NEC Corporation (“NEC”), in collaboration with NTT, Inc. (“NTT”), have developed a technology (the “Technology”) that enables multiple vehicles traveling at high speed to simultaneously maintain stable communications using high-capacity millimeter-wave (mmWave) communications in the 40 GHz band, which is expected to be leveraged in the 6G era. The Technology combines distributed MIMO*1, in which multiple antennas are distributed and deployed from a base station, with a technique that pre-compensates the transmit frequency and transmit timing of signals.In a demonstration trial conducted in March 2026 at a full-scale tunnel test facility installed at Japan's National Institute for Land and Infrastructure Management (NILIM), Ministry of Land, Infrastructure, Transport and Tourism, multiple vehicles equipped with mobile terminals traveling at high speed in opposite lanes maintained stable, high throughput, achieving approximately a 1.3x improvement in average throughput compared to the conventional method. This result represents a major step toward social implementation of 6G-related technologies—including implementation in new base stations—such as autonomous driving utilizing mmWave and improved in-vehicle user experiences.Video of the demonstration:BackgroundWhen communicating in high-frequency bands such as mmWave in high-mobility environments (e.g., automobiles and trains traveling at high speed), frequent base station switching occurs, causing abrupt changes in Doppler frequency*2 and propagation delay, which degrades communication quality. DOCOMO, NEC, and NTT have been working to address this challenge, and in March 2025 successfully demonstrated*3 a technology that stabilizes communication quality for a single vehicle traveling at high speed by compensating the transmit frequency and transmit timing on the base station side.In the present demonstration, the March 2025 technology was further advanced to develop a method that suppresses communication-quality degradation for multiple mobile-terminal-equipped vehicles even under complex conditions, where multiple mobile-terminal-equipped vehicles traveling at high speed in opposite lanes communicate simultaneously.Key Features of the TechnologyUsing uplink reference signals transmitted from each mobile-terminal-equipped vehicle, the Technology pre-estimates, for each base-station antenna, the appropriate transmit frequency and transmit timing for each vehicle. It then pre-compensates each vehicle-specific signal and then combines (multiplexes) the signals for transmission. This eliminates differences in received frequency and received timing that occur during antenna switching for multiple mobile-terminal-equipped vehicles simultaneously, thereby stabilizing high-capacity mmWave communications for multiple vehicles.Figure 1. Transmit frequency and transmit timing pre-compensation technology for multiple mobile-terminal-equipped vehiclesDemonstration Trial and ResultsFrom March 26 to 27, 2026, DOCOMO, NEC, and NTT conducted a demonstration trial of the Technology at the full-scale tunnel test facility at NILIM. To emulate a high-mobility environment, three distributed antennas of a base station were installed along one side of a road at 150-meter intervals. A downlink transmission experiment was then conducted with two mobile-terminal-equipped vehicles traveling at 60 km/h in opposite lanes.Under more complex conditions than the March 2025 demonstration, the conventional method (in which only the terminal side compensates received frequency and received timing) was compared with the proposed Technology (which additionally applies per-vehicle pre-compensation on the base-station side before combining and transmitting the signals). The comparison used the combined (sum) throughput of the two mobile-terminal-equipped vehicles.To validate performance in a more complex radio propagation environment, the experiment was conducted inside a tunnel, where radio waves reflect and antenna switching occurs frequently while driving. When only the conventional method was applied, the combined throughput dropped significantly during antenna switching—from 550 Mbps to approximately 110 Mbps—and the average throughput over 30 seconds of driving was approximately 430 Mbps. In contrast, when the Technology was applied, throughput degradation during antenna switching was suppressed, maintaining a stable throughput of at least 380 Mbps, and the average throughput improved to 560 Mbps, representing an approximate 1.3x increase over the conventional method. In addition, the 5th-percentile throughput value in the cumulative distribution function (CDF) improved from 270 Mbps (conventional) to 480 Mbps (proposed), an approximate 1.8x increase.Figure 2. Demonstration trial imageFigure 3. Experimental result (1): Combined throughput comparisonbetween the conventional method and the proposed TechnologyFigure 4. Experimental result (2): CDF of combined throughput comparison between the conventional method and the proposed TechnologyFuture DevelopmentThis demonstration confirmed that high-capacity and stable communications using mmWave distributed MIMO are possible even in high-mobility environments where multiple automobiles and trains travel at high speed, representing a major step toward real-world deployment. With the success of this demonstration, the use of mmWave communications is expected in applications such as immersive services including in-vehicle XR (extended reality), real-time translation and guidance leveraging generative AI, and sensor-data coordination for cooperative autonomous driving.Going forward, DOCOMO, NEC, and NTT will conduct further trials assuming various real-world environments such as high-speed railways, conventional railways, and arterial roads, and will contribute to realizing stable high-capacity communications in the 6G era.The trial and its results will be exhibited at Wireless Japan × Wireless Technology Park (WTP) 2026*4 (May 27-29, 2026, Tokyo Big Sight, West 3 & 4 Hall; XG Mobile Promotion Forum exhibit area) and Tsukuba Forum 2026*5 (May 27-28, 2026, NTT Tsukuba R&D Center, R-1 Area; NTT Exhibits).*1 Distributed MIMO (Multiple-Input Multiple-Output) :A technology in which a large number of antennas are distributed from a single base station within an area for MIMO transmission between each antenna and a mobile terminal within the area.*2 Doppler Frequency: A frequency shift that occurs when a radio transmitter is moving. The received frequency increases when approaching the receiver and decreases when moving away.*3 NTT Corp., NTT DOCOMO and NEC Demonstrate Distributed MIMO Technology for High-Frequency 6G Communications in Automobiles and Trains(March 25, 2025)*4 Wireless Japan×Wireless Technology Park (WTP) 2026: https://wjwtp.jp/2026/en/*5 Tsukuba Forum 2026: https://www.rd.ntt/e/as/tforum/About NTT DOCOMONTT DOCOMO, Japan's leading mobile operator with over 93 million subscribers, is one of the global leaders in 3G, 4G and 5G mobile network technologies.Under the slogan “Bridging Worlds for Wonder & Happiness,” DOCOMO is actively collaborating with global partners to expand its business scope from mobile services to comprehensive solutions, aiming to deliver unsurpassed value and drive innovation in technology and communications, ultimately to support positive change and advancement in global society. https://www.docomo.ne.jp/english/About NEC CorporationThe NEC Group leverages technology to create social value and promote a more sustainable world where everyone has the chance to reach their full potential. NEC Corporation was established in 1899. Today, the NEC Group's approximately 110,000 employees utilize world-leading AI, security, and communications technologies to solve the most pressing needs of customers and society. For more information, visit NEC at https://group.nec/global/en/. Copyright 2026 JCN Newswire via SeaPRwire.com. All rights reserved. www.jcnnewswire.com

KAWASAKI, Japan, May 25, 2026 - (JCN Newswire via SeaPRwire.com) - Fujitsu Limited today announced the development of a self-evolving multi-AI agent technology[1] that enables multiple AI agents to perform tasks as a team, continuously and safely learning from daily execution results, human feedback, policy revisions, and specification changes.In corporate operations, legal revisions, system changes, specification updates, and on-site rule modifications occur continuously. In business operations involving vast amounts of documents and design specifications, determining which information to reference, which judgment criteria to prioritize, and the extent of impact to confirm when responding to business needs has traditionally relied on the experience and tacit knowledge of skilled professionals. In addition, from a system perspective, continuous adjustments by experts were necessary to reflect changes in prompts, search methods, evaluation criteria, and operational rules.While conventional AI agents demonstrate high processing capabilities for given instructions, they have found it difficult to independently analyze reasons for failure and safely incorporate them into subsequent operations. Consequently, adapting AI agents to the latest business environments required experts to continuously adjust prompts, search methods, evaluation criteria, and operational rules.To address this challenge, Fujitsu has developed a technology that allows AI agents to safely learn by autonomously verifying their operational experience.This technology is a multi-AI agent technology that continuously and safely evolves by adapting to changes in business environments and incorporating business execution results, human feedback, institutional revisions, and specification changes. Its most significant feature is that while performing tasks, AI agents identify reasons for success and failure, extract actionable knowledge and operational insights, and do not simply store generated improvement proposals. This allows AI agents to take over tasks such as prompt adjustments and evaluation criteria updates, which were previously performed continuously by experts. Furthermore, by deploying AI within the customer's environment, it continuously adapts to individual rules and judgment criteria that arise during business operations, realizing a business foundation that evolves with people and the environment. Figure 1: Overview of the technologyFujitsu applied this newly developed technology to the following two areas and evaluated its accuracy:1. Automated enhancement and continuous evolution of business-specific LLMsThis technology can be applied to the entire process of building business-specific LLMs. Multi-AI agents autonomously execute and optimize a series of steps such as data selection, adjustment of learning conditions, evaluation, and improvement, which were previously handled by experts. Each AI agent generates improvement proposals based on business execution results and evaluation results, and by verifying and reflecting only the effective ones, it continuously improves model performance. This significantly reduces the need for tasks previously performed by experts, realizing a mechanism where AI autonomously continues to evolve within business operations.Figure 2: Automated enhancement and continuous improvement of business-specific LLMs by self-evolving multi-AI agent technologyFujitsu automatically enhanced "Takane" for multiple domains such as manufacturing, healthcare, finance, and public administration, and continuously improved it through operations. Through operational use, continuous improvements were implemented, resulting in a significant average accuracy improvement of 28 points compared to pre-specialization performance. In the medical field, for example, the application of this technology enables structured extraction of information tailored to specific business operations, such as extracting diagnostic names, progression stages, and treatment policies from unstructured data such as medical records and test results, in a consistent format. This not only improves the accuracy of responses but also demonstrates that multi-AI agents can automate the process of building and improving business-specific LLMs, which previously required design and adjustment based on specialized knowledge, and continuously optimize them through operations. This enables companies to build AI tailored to their own operations in a short period and continuously improve it in response to changes in operations, without heavily relying on AI specialists.Figure 3: Benchmark evaluation results before and after domain-specific optimizationFurthermore, under the OneFujitsu initiative[2], Fujitsu has been promoting the global standardization of internal IT, data, and business processes. By globally applying this technology and the multi-AI agent platform equipped with "Takane," Fujitsu is achieving autonomous business execution and accelerating management speed, thereby accelerating the transition to Data & AI-driven management.2. Application to Design Specification Search in Large-Scale Business SystemsThis technology was applied to AI agent-based document search for design specifications of Fujitsu's electronic health record system for medium-to-large hospitals and business solutions for local governments. Traditionally, identifying the scope of impact for software modifications due to legal revisions or policy changes required skilled experts with deep knowledge of regulations, business processes, and system architecture. With this technology, AI agents now learn from past search results, failure cases, and human corrections. As a result, they autonomously improve search range expansion and document extraction strategies. This reduces the effort required for designing and improving search logic and also improves accuracy. This indicates that AI agents did not merely repeat searches, but learned and applied the exploration techniques used by skilled experts, such as checking related peripheral documents during operations and not excluding seemingly irrelevant documents if they belong to the same business domain. Going forward, Fujitsu will apply these insights and technologies to its AI-Driven Software Development Platform to further enhance and streamline the overall design and development process.Future plansFujitsu plans to integrate this technology into its proprietary AI platform and offer it as a core technology to support the in-house development and autonomous operation of business-specific AI. Furthermore, as one of the advanced AI technologies within the Fujitsu Kozuchi AI platform, Fujitsu will promote its application to a wide range of areas requiring specialized knowledge and continuous improvement.Additionally, by combining insights from joint research with Associate Professor Graham Neubig and Assistant Professor Tim Dettmers from Carnegie Mellon University and Fujitsu's developed generative AI reconstruction technology, Fujitsu will advance the development of technology to operate self-evolving multi-AI agent systems with less memory and power. This aims to enable the use of AI teams that continuously learn from operations not only in cloud environments but also in highly confidential on-premises and edge environments.Fujitsu aims to realize sovereign AI that can continuously learn not only in the cloud but also in on-premises and edge environments. Furthermore, by enabling AI to learn from on-site failures, human instructions, and environmental changes in real-time, and safely apply this knowledge to subsequent tasks, Fujitsu will evolve AI into an intelligent foundation that grows with the workplace. This will solve societal challenges such as the shortage of specialized personnel, adaptation to regulatory changes, succession of tribal knowledge, and advancement of on-site operations, thereby creating a future where people and AI learn from each other to evolve entire industries.[1] Self-evolving multi-AI agent technology:A technology where multiple AI agents divide tasks, execute them, and continuously and safely improve their subsequent actions based on the results, human feedback, and environmental changes such as policy revisions and specification changes.[2] OneFujitsu initiative: An initiative to define common global standard processes (Golden Standard) across Fujitsu, unifying and optimizing operations across organizations and countries.Fujitsu’s Commitment to the Sustainable Development Goals (SDGs)The Sustainable Development Goals (SDGs) adopted by the United Nations in 2015 represent a set of common goals to be achieved worldwide by 2030.Fujitsu’s purpose - “to make the world more sustainable by building trust in society through innovation” — is a promise to contribute to the vision of a better future empowered by the SDGs.About FujitsuFujitsu’s purpose is to make the world more sustainable by building trust in society through innovation. As the digital transformation partner of choice for customers around the globe, our 100,000 employees work to resolve some of the greatest challenges facing humanity. Our range of services and solutions draw on five key technologies: AI, Computing, Networks, Data & Security, and Converging Technologies, which we bring together to deliver sustainability transformation. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.5 trillion yen (US$23 billion) for the fiscal year ended March 31, 2026 and remains the top digital services company in Japan by market share. Find out more: global.fujitsuPress ContactsFujitsu LimitedPublic, Investor and Analyst Relations DivisionInquiries Copyright 2026 JCN Newswire via SeaPRwire.com. All rights reserved. www.jcnnewswire.com