SNS Telecom & IT’s latest research report, “Private LTE & 5G Network Ecosystem: 2025 – 2030” indicates that the private LTE and 5G network market is estimated to be worth $7.2 billion by the end of 2028 and continues to grow as private 5G deployments overtake LTE across many vertical industries. This steady, strong growth stands in contrast to the tepid pace of infrastructure spending in the much larger but relatively stagnant public mobile network market, where standalone 5G core investments are growing but RAN (Radio Access Network) sales remain flat following a sharp decline last year.
Against this backdrop, the real-world impact of private networks – spanning both facility/campus-based and wide area deployments – is clearly visible across a diverse range of customers, from manufacturers, port operators, and airlines to sports clubs and public sector organizations.
Among many other impactful examples, Tesla, LG Electronics, and Hyundai have eliminated connection-related AGV (Automated Guided Vehicle) stoppages at their production facilities; Peel Ports Group has experienced a tenfold increase in network performance at the Port of Liverpool’s metal-heavy environment, which previously hindered Wi-Fi connectivity; Lufthansa has achieved a 75% improvement in operational process speed at its LAX cargo facility; partially sighted fans are able to experience football matches in exceptional detail using private 5G-connected headsets at Crystal Palace Football Club’s Selhurst Park stadium; and police forces in Ontario’s Peel-Halton Region have had uninterrupted in-vehicle data access – especially during outages affecting public mobile operator services – since adopting their independent public safety broadband network, which has recently undergone a 5G core upgrade.
While these practical and tangible benefits are already compelling, another sign of the market’s positive momentum is how customers are increasingly incorporating private 5G networks as a key component of their new facilities. A recent case in point is GDC’s (Georgia Department of Corrections) new state prison project, which also involves the implementation of a secure and physically isolated private 5G network using Band n48 (3.5 GHz) CBRS spectrum to provide indoor and outdoor coverage at a greenfield prison campus with 13 buildings covering 800,000 square feet across 200 acres. Examples of other facilities where private 5G networks have been or are being deployed from the outset include Hitachi Rail’s Hagerstown factory, Hyundai Motor’s HMGMA electrified vehicle plant, Los Angeles Chargers’ El Segundo training facility, Formula 1’s Las Vegas complex, Cleveland Clinic’s Mentor Hospital, CHI’s (Children’s Health Ireland) New Children’s Hospital, Port of Aberdeen’s South Harbour, NEC’s Kakegawa plant; Pegatron’s Batam smart factory, PATTA’s low-carbon Renwu factory, and Jacto’s Paulópolis production facility.
SNS Telecom & IT’s “Private LTE & 5G Network Ecosystem: 2025 – 2030” report projects that global spending on private LTE and 5G network infrastructure for vertical industries will grow at a CAGR of approximately 22% between 2025 and 2028, eventually exceeding $7.2 billion by the end of 2028. More than 70% of these investments – an estimated $5.1 billion – will be directed towards the buildout of standalone private 5G networks, which are well-positioned to become the predominant wireless connectivity medium for Industry 4.0 applications in manufacturing and process industries, as well as critical communications over mission-critical broadband networks for sectors such as public safety, defense, utilities, and transportation. This unprecedented level of growth is likely to transform the private RAN, mobile core, and transport network segments into an almost parallel equipment ecosystem to public mobile operator infrastructure in terms of market size by the late 2020s. By 2030, private networks could account for as much as a fourth of all mobile network infrastructure spending.
About SNS Telecom & IT:
SNS Telecom & IT is a global market intelligence and consulting firm with a primary focus on the telecommunications and information technology industries. Developed by in-house subject matter experts, our market intelligence and research reports provide unique insights on both established and emerging technologies. Our areas of coverage include but are not limited to 6G, 5G, LTE, Open RAN, vRAN, small cells, mobile core, xHaul transport, network automation, mobile operator services, FWA, neutral host networks, private 4G/5G cellular networks, public safety broadband, critical communications, MCX, IIoT, V2X communications, and vertical applications.
AI’s roles in IMT‑2030
To date, the main benefit of AI for telecom has been to reduce headcount/layoff employees. Light Reading’s Iain Morris wrote, “Telecom operators and vendors, nevertheless, are already using AI as the excuse for thousands of job cuts made and promised. So far, those cuts have not brought any improvement in the sector’s fortunes. Meanwhile, ceding basic but essential skills to systems that hardly anyone understands seems incredibly risky.” Some say that will change with 6G/ IMT 2030, but that’s a long way off. Others point to AI RAN, but that has not gotten any real market traction with wireless telcos.
As Gen AI development accelerates, robust wireless and fiber optic network infrastructure will be essential to accommodate the substantial data and communication volume generated by AI systems. Initially, the existing network ecosystem—encompassing wireless, wireline, broadband, and satellite services—will absorb this traffic load. However, the expanding requirements of AI are anticipated to drive the future emergence of entirely new network architectures and communication paradigms.
For sure, AI needs massive, fast, reliable connectivity to function, driving demand for low latency optical networks and 6G/ IMT 2030, which AI itself will optimize, leading to better efficiency, security, resource management, and new services like real-time AR/VR, ultimately boosting telecom revenue and innovation across the entire digital ecosystem.
Source: Pitinan Piyavatin/Alamy Stock Photo
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- AI Backend Scale-Out and Scale-Up Networks: These are specialized, private networks within and across data centers designed to connect numerous GPUs and enable them to function as one massive compute resource. They utilize technologies like:
- InfiniBand: A long-standing high-bandwidth, low-latency technology that has become a top choice for connecting GPU clusters in AI training environments.
- Optimized Ethernet: Ethernet is gaining ground for AI workloads through the development of enhanced, open standards via the Ultra Ethernet Consortium (UEC). These enhancements aim to provide lossless, low-latency fabrics that can match or exceed InfiniBand’s performance at scale.
- High-Speed Optics: The use of 400 Gbps and 800 Gbps (and soon 1.6 Tbps) optical interconnects is critical for meeting the massive bandwidth and power requirements within and between AI data centers.
- Edge AI Networking: As AI inferencing (generating responses from AI models) moves closer to the end-user or device (e.g., in autonomous vehicles, smart hospitals, or factories), specialized edge networks are needed. These networks must ensure low latency and localized processing to enable real-time responses.
- AI-Native 6G Networks: The upcoming sixth-generation (6G) wireless networks are being designed with AI integration as a core principle, rather than an add-on.
- These networks are expected to be fully automated and self-evolving, using AI to optimize resource allocation, predict issues, and enhance security autonomously.
- They will support extremely high data rates (up to 1 Tbps), ultra-low latency (around 1 ms), and new technologies like AI-RAN (Radio Access Network) that integrate AI capabilities directly into the network infrastructure.
- More in next section below.
- Self-Evolving Networks: The ultimate goal is the development of “self-evolving networks” where AI agents manage and optimize the network infrastructure autonomously, adapting to new demands and challenges without human intervention.
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In IMT 2030/6G networks, AI will shift from being an “add-on” optimization tool (as in 5G) to a native, foundational component of the entire network architecture. This deep integration will enable the network to be self-organizing, highly efficient, and capable of supporting advanced AI applications as a service. Native AI for IMT-2030 (6G) means building AI directly into the network’s core architecture, making it AI-first and pervasive, rather than adding AI as an overlay; this enables self-optimizing, intelligent networks that can autonomously manage resources, provide ubiquitous AI services, and offer seamless, context-aware experiences with minimal human intervention, fundamentally transforming both network operations and user applications by 2030.
- Ubiquitous Intelligence: Embedding AI everywhere, enabling distributed intelligence for AI model training, inference, and deployment directly within the network infrastructure, extending to the network edge.
- Autonomous Operations: AI handles complex tasks like network optimization, resource allocation, and automated maintenance (O&M) in real-time, reducing reliance on manual intervention.
- AI-as-a-Service (AIaaS): The network transforms into a unified platform providing both communication and AI capabilities, making AI accessible for various applications.
- Intelligent Processing: AI drives functions across the air interface, resource management, and control planes for highly efficient operations.
- Data-Driven Automation: Leverages big data and real-time analytics to predict issues, optimize performance, and automate complex decision-making.
- Seamless User Experience: Moves beyond touchscreens to AI-driven interactions, offering more natural and contextual computing.
- Autonomous Operations: AI will enable self-monitoring, self-optimization, and self-healing networks, drastically reducing the need for human intervention in operation and maintenance (O&M).
- Dynamic Resource Management: ML algorithms will analyze massive amounts of network data in real-time to predict traffic patterns and user demands, dynamically allocating bandwidth, power, and computing resources to ensure optimal performance and energy efficiency.
- AI-Native Air Interface: AI/ML models will replace traditional, manually engineered signal processing blocks in the physical layer (e.g., channel estimation, beam management) to adapt dynamically to complex and time-varying wireless environments, improving spectral efficiency.
- Enhanced Security: AI will be critical for real-time threat detection and automated incident response across the hyper-connected 6G ecosystem, identifying anomalies and mitigating security risks that are not well understood by current systems.
- Digital Twins: AI will power the creation and management of real-time digital twins (virtual replicas) of the physical network, allowing for sophisticated simulations and testing of network changes before real-world deployment.
- Pervasive Edge AI: AI model training and inference will be distributed throughout the network, from the cloud to the edge (devices, base stations), reducing latency and enabling real-time, localized decision-making for applications like autonomous driving and industrial automation.
- Support for Advanced Use Cases: The massive data rates (up to 1 Tbps), ultra-low latency, and high reliability enabled by AI in 6G will facilitate new applications such as holographic communication, remote robotic surgery with haptic feedback, and collaborative robotics that were not feasible with 5G.
- Federated Learning: The network will support distributed machine learning techniques, such as federated learning, which allow AI models to be trained on local data across various devices without the need to centralize sensitive user data, thus ensuring data privacy and security.
- Integrated Sensing and Communication (ISAC): AI will process the rich environmental data gathered through 6G’s new sensing capabilities (e.g., precise positioning, motion detection, environmental monitoring), allowing the network to interact with and understand the physical world in a holistic manner for applications like smart city management or augmented reality.
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AI‑native air interface and RAN:
IMT‑2030 explicitly expects a new AI‑native air interface that uses AI/ML models for core PHY/MAC functions such as channel estimation, symbol detection/decoding, beam management, interference handling, and CSI feedback. This enables adaptive waveforms and link control that react in real time to channel and traffic conditions, going beyond deterministic algorithms in 5G‑Advanced.
At the RAN level, IMT‑2030 envisions “native‑AI enabled” architectures that are simpler but more intelligent, with data‑driven operation and distributed learning across gNBs, edge nodes, and devices. AI/ML will be applied end‑to‑end for resource allocation, mobility, energy optimization, and fault management, effectively turning the RAN into a self‑optimizing, self‑healing system.
Integrated AI and communication services:
The framework defines “Artificial Intelligence and Communication” (often phrased as Integrated AI and Communication) as a specific usage scenario where the network provides AI compute, model hosting, and inference as a service. Example use cases include IMT‑2030‑assisted automated driving, cooperative medical robotics, digital twins, and offloading heavy computation from devices to edge/cloud via the 6G network.
To support this, IMT‑2030 includes “applicable AI‑related capabilities” such as distributed data processing, distributed learning, AI model execution and inference, and AI‑aware scheduling as native capabilities of the system. Computing and data services (not just connectivity) are treated as integral IMT‑2030 components, especially at the edge for low‑latency, energy‑efficient AI workloads.
System intelligence and new use cases:
AI is central to several new IMT‑2030 usage scenarios beyond classic eMBB/mMTC/URLLC, including Immersive Communication, Integrated Sensing and Communication, and Integrated AI and Communication. In integrated sensing, AI fuses multi‑dimensional radio sensing data (position, motion, environment, even human behavior) to provide contextual awareness for applications like smart cities, industrial control, and XR.
Embedding intelligence across air interface, edge, and cloud is seen as necessary to manage 6G complexity and enable “Intelligence of Everything,” including real‑time digital twins and AIGC‑driven services. The vision is for the 6G/IMT‑2030 network to act as a distributed neural system that tightly couples communication, sensing, and computing.
IMT 2030 Goals:
- To create self-healing, self-optimizing networks that can adapt to diverse demands.
- To enable new AI-driven applications, from intelligent digital twins to advanced immersive experiences.
- To build a truly intelligent communication fabric that supports a hyper-connected, AI-enhanced world.
Summary table: AI’s roles in IMT‑2030:
| Dimension | AI role in IMT‑2030 |
|---|---|
| Air interface | AI‑native PHY/MAC for channel estimation, decoding, beamforming, interference control. |
| RAN/core architecture | Native‑AI enabled, data‑driven, self‑optimizing/self‑healing network functions. |
| Compute and data services | Built‑in edge/cloud compute for AI training, inference, and data processing. |
| Usage scenarios | Dedicated “Integrated AI and Communication” plus AI‑rich sensing and immersive use cases. |
| Applications and ecosystems | Support for digital twins, automated driving, robotics, AIGC, and industrial automation. |
In summary, AI in IMT‑2030 is both an internal engine for network intelligence and an exported capability the network offers to verticals, making 6G effectively AI‑native end‑to‑end.
Source IEEE ComSoc Technology Blog: https://techblog.comsoc.org/ – by Alan Weissberger – December 13th and 12th 2025
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