Tokyo 2040: From Reactive Networks to Living Digital Twins — How Open RAN and Holographic Societies Will Reshape Connectivity

Nicolas Waern / WINNIIO AB / ORCID: 0009-0001-4011-8201 Corresponding author: ceo@winniio.io / License: CC-BY-4.0 / Published: 2026-04-28

The Network That Shouldn't Work — But Does

Shinjuku Station. 3.64 million passengers pass through daily. On any given Tuesday morning, 200,000 people are simultaneously streaming, calling, uploading, navigating. Rakuten Mobile's Open RAN network handles it. Mostly.

But "mostly" is where the interesting engineering lives.

Today, when a Rakuten base station fails, or a handover drops a call between two virtualized nodes, the response follows a familiar pattern: alert fires, engineer investigates, configuration adjusts, fix deploys. The cycle takes minutes to hours. The network is reactive — excellent at recovering, incapable of anticipating.

Rakuten Mobile built something genuinely historic. The world's first fully virtualized, cloud-native mobile network. No proprietary hardware lock-in. Software-defined radio access. Every layer disaggregated. It was the Open RAN bet before Open RAN was fashionable, and it changed how the industry thinks about network architecture.

But the Rakuten network today, impressive as it is, operates like a patient who visits the doctor after symptoms appear. What comes next is the network equivalent of continuous health monitoring, predictive intervention, and eventually — biological-level self-regulation.

That is not a metaphor. It is the architectural trajectory.

This article maps that trajectory through the lens of SMILE — Sustainable Methodology for Impact Lifecycle Enablement [1] — a benefits-driven digital twin framework that sequences implementation from Reality Emulation through Perpetual Wisdom. The 2025–2040 arc is not a prediction. It is a phase diagram. We are already in Phase 1. The question is how fast we move through the remaining five.

2025–2028: The Virtual Twin Era — Reality Emulation and Concurrent Engineering

Simulation Before Intervention

The first transformation is already underway in research labs and forward-thinking operator teams: replacing the "touch the live network and see what happens" approach with digital twin environments where every configuration change, software update, and traffic scenario can be tested before it ever reaches production.

This maps directly to the first two SMILE phases. Reality Emulation asks: where are we, who are the stakeholders, and what shared canvas do we agree on? For a telecom operator in 2025, that means a geometrically accurate model of the coverage environment, a stakeholder map that includes the city authority, the device manufacturers, and the end users — not just the network engineers — and a virtual-first mindset that treats simulation as the primary workspace. Concurrent Engineering then narrows the scope: define the Minimal Viable Twin (MVT), validate the hypothesis against real traffic data, establish the as-is before designing the to-be [1][2].

This matters more in Open RAN than anywhere else. When you disaggregate the RAN — splitting the radio unit, distributed unit, and central unit from different vendors — the interaction surface explodes. A software update from one vendor can interact unexpectedly with another vendor's scheduler. Traditional networks caught this in painful post-deployment incidents. Virtual twin labs catch it before the maintenance window opens.

The technical foundation is already available. Tools like Siemens' Xcelerator, NVIDIA's Omniverse, and emerging telecom-specific platforms can model RF propagation, traffic load, and network topology with sufficient fidelity to make pre-deployment testing meaningful. The gap is integration: connecting these simulation environments to real operator data, real traffic patterns, real site geometries. Grieves [3] traced the concept of the digital twin back to product lifecycle management in 2014; the same principle applies here — the twin is most useful when it mirrors the real system's data, not a sanitized approximation of it.

AI/ML That Learns in Safe Environments

The second shift is where AI/ML becomes genuinely useful rather than decoratively present in vendor slide decks. Today most "AI-powered" network optimization means: collect KPIs, train a model offline, deploy a policy, measure results, iterate over weeks. The feedback loop is slow, the model is always slightly out of date, and the training environment is the live network.

Virtual twins break this. A model trained in a high-fidelity twin environment can explore millions of traffic scenarios in hours. It can experience a major sporting event at the National Olympic Stadium without waiting for the next World Cup. It can simulate the RF effects of a new high-rise construction before the building exists. Then it deploys to inference only — touching the live network — when it has already proven itself against scenarios the live network has never seen.

This is the architecture that matters: train in twins, infer in production.

Open RAN Enables What Closed Systems Cannot

Here is the structural advantage that makes all of this possible at scale: Open RAN's vendor-neutral architecture means the digital twin layer is not owned by the radio vendor. In a traditional Nokia or Ericsson deployment, the vendor's element management system is the authoritative source of truth. The operator depends on that vendor for optimization tooling, for simulation environments, for AI/ML capabilities. The twin is built into the vendor's walled garden.

Open RAN separates the intelligence from the infrastructure. The RAN Intelligent Controller (RIC) is an open interface [8]. The digital twin layer sits alongside it, vendor-neutral, reading from any source, optimizing across any stack. The operator owns the intelligence. This is not a technical detail — it is a sovereignty question.

WINNIIO's role in this era is outcome-first methodology applied to the twin buildout itself. We have built digital twins across domains — buildings, cities, biological systems. The core lesson from every domain is identical: start with the decision you need to make, work backward to the data required to make it confidently, then build the minimum viable twin that answers that question. Not the most comprehensive model. The most useful one.

For telecom in 2025–2028, the first useful twins are targeted: handover optimization at dense urban sites, energy efficiency during low-traffic periods, capacity prediction for planned events. Small, precise, decision-driving.

2028–2033: The Dynamic Twin Era — Collective Intelligence and Contextual Intelligence

Sub-Second State, Closed-Loop Intelligence

By 2028–2030, the question will no longer be whether to build a digital twin for the RAN. It will be whether the twin updates fast enough to be useful in real-time closed-loop control.

We are now in SMILE's Collective Intelligence phase: physical sensors are deployed, ontologies are created so that different data sources speak the same language, and remote enablement allows the twin to absorb state from assets it has never directly observed [1]. This transitions quickly into Contextual Intelligence — connected everything, command and control, real-time decisions, predictive analytics. The twin is no longer a simulation tool. It is the intelligence layer of a live system.

The shift from virtual twins (simulation environments) to dynamic twins (live system mirrors with sub-second state updates) requires a different data architecture. Every base station's current load, interference conditions, antenna configuration, and neighboring cell state must flow continuously into the twin. Not in batch exports. Continuously.

This is not a storage problem — it is a streaming and inference problem. The twin must process state updates, run optimization logic, and feed control decisions back to the RIC faster than the network conditions change. At dense urban sites, that means sub-second latency between observation and intervention. Tao et al. [4] demonstrated in industrial contexts that dynamic digital twins operating at this cadence outperform static simulation environments by an order of magnitude in decision quality — the principle transfers directly to network operations.

The closed loop looks like this: the live RAN generates telemetry → the dynamic twin updates its state → an optimization model running against the twin proposes a configuration change → the RIC validates and applies it → the twin observes the result → the model updates. No human in the loop for routine decisions. Humans set the policy, define the boundaries, review the decisions. The loop runs autonomously within those bounds.

Physics Replaces Drive Tests

One of the most expensive and least scalable activities in mobile network operations is the drive test: a technician in a vehicle with measurement equipment, driving routes through coverage areas, collecting signal quality data. Rakuten Mobile's network covers dense urban Japan. Drive testing that comprehensively is never completed — it is sampled, extrapolated, and assumed.

3D environment reconstruction changes this. Gaussian splatting [5] and NeRF (Neural Radiance Fields) [6] techniques can now generate photorealistic, geometrically accurate 3D models of urban environments from drone footage and satellite imagery. Combined with physics-based RF propagation modeling, the twin can predict signal strength, interference patterns, and coverage holes with accuracy that rivals physical measurement — and updates when the environment changes.

A new tower goes up in Shibuya. The twin recalculates the RF environment, identifies the coverage and interference implications, and suggests beam configuration adjustments — before the first user experiences the change. The drive test becomes a validation step, not a discovery step.

Convergence: The City Becomes the Twin

The most significant development of the 2028–2033 period is not a telecom innovation. It is convergence.

Tokyo's metropolitan government has been building city-scale digital twins since the early 2020s [12]. Mobility data. Energy demand. Building occupancy. Weather modeling. Each of these systems, individually, is useful. Together, they are predictive in ways that none are alone.

A telecom network that knows a concert at the Tokyo Dome is sold out, that the Yamanote Line is running at 140% capacity on the approach, and that current temperature will drive high device usage for navigation and streaming — that network can pre-position capacity before the demand arrives. It can pre-load edge caches. It can adjust beam patterns for the crowd geometry.

None of this is possible if the telecom twin is isolated. All of it becomes possible when telecom twins connect to city twins.

WINNIIO's role in this era is the orchestration layer. We do not own the city twin or the telecom twin. We connect them. Cross-domain twin expertise — the methodology for making twins from different domains speak to each other without losing the integrity of either — is the scarcest capability in this landscape.

The Network as Actant

Bruno Latour's actor-network theory [13] offers a useful lens here that the engineering literature rarely reaches for. Latour argued that non-human entities — machines, documents, protocols — can be actants: participants in a network that shape outcomes as much as any human does. In a traditional centralized network, the infrastructure is passive substrate. The engineer acts; the network is acted upon.

The converged twin-network described above dissolves this distinction. The network, through its twin layer, proposes configuration changes, anticipates demand, negotiates resources, and adapts to context. It is not background infrastructure. It is an active participant in the sociotechnical ecosystem of the city. Latour would recognize this immediately: the network has become an actant. Treating it as such — giving it the ontological status of a participant rather than a tool — has direct architectural consequences. It means designing interfaces for the network to communicate its state and preferences outward, not just to consume commands from operators. The O-RAN RIC is the first formal expression of this design principle in the telecom world [8][9].

2033–2040: The Holographic Society — Continuous Intelligence and Perpetual Wisdom

Networks That Must Know What Will Happen

Holographic communication is not a gimmick. It is the logical endpoint of high-fidelity remote presence — and it places requirements on networks that current architectures cannot satisfy. According to ITU-T specifications on holographic-type communication [10], a holographic stream requires sustained throughput of 10 Gbps per session, sub-1ms end-to-end latency, jitter below 0.1ms, and packet loss no greater than 10⁻⁷. To put that in context: today's best 5G networks deliver around 1 Gbps peak with 10ms latency under ideal conditions. The gap is not incremental — it demands a fundamentally different operating model.

You cannot manage a network to that standard reactively. By the time you detect a problem and respond, the holographic session is corrupted. The only viable operating model is anticipatory: the network must predict demand before users generate it and allocate resources before they are needed.

This requires a network that is, itself, a digital twin. Not a network with a twin attached — a network where the twin and the infrastructure are a single unified system. The twin's predictions drive resource allocation. The infrastructure's state continuously validates the twin's model. They are the same thing viewed from different angles.

This is SMILE's Continuous Intelligence phase: prescriptive maintenance, AI-driven prognostics, a universal event pipeline, simulate everything [1]. Nokia Bell Labs [11] and Samsung [14] both project 6G architectures in this direction — not just faster radio but native intelligence embedded in the network fabric. The 6G timeline (2030+ commercial) aligns precisely with the Continuous Intelligence horizon.

Sovereign Community Nodes and Perpetual Wisdom

Centralized infrastructure has a scaling ceiling. As more services require local data sovereignty — health data, financial data, personal communications — the architecture of centralized cloud with long-haul backhaul becomes both a technical and a political liability.

By 2033–2040, sovereign community nodes emerge as a structural response. Local compute, local inference, local data. The community node is a digital twin of the community itself — its health patterns, its energy consumption, its communication needs — running locally, syncing selectively, controlled by the people who live there.

This is SMILE's Perpetual Wisdom phase: ecosystem enablement, shared impact globally, circular strategies, open-source contribution [1][2]. The methodology's endpoint is not a finished product — it is a self-sustaining loop where the insights generated by the system feed back into improving the system, and where the value created is shared outward to adjacent communities rather than captured in a vendor's licensing model.

This is not a decentralization fantasy. It is the extension of the same principle Rakuten demonstrated with Open RAN: disaggregate the stack, open the interfaces, return control to the operator. Except the operator is now the community.

Tokyo as the First Twin-Native City

By 2040, Tokyo will likely be the first city where every infrastructure layer — transport, energy, health, telecom — runs synchronized digital twins, coordinated at the city level. Not because Tokyo planned it that way from the beginning, but because the convergence became inevitable once each layer's twin was mature enough to expose APIs.

The experience of moving through this city will be qualitatively different. The network does not cover you like a blanket — it anticipates you like an environment. Bandwidth is allocated where you are going, not just where you are. The handover between cells happens before you reach the cell boundary, because the system knows your trajectory. The building you are entering has already signaled its indoor topology to the network, which has already reconfigured its beamforming.

This is not "experience fabric" as a marketing phrase. It is a specific technical architecture: synchronized twins enabling anticipatory resource allocation across domains. Batty [12] argued that smart cities are fundamentally about using data flows to make cities legible to themselves — able to observe and respond to their own state. What 2040 Tokyo represents is the next step: cities that do not merely observe but anticipate.

Life Atlas and LPI: The Sovereign Personal Layer

There is a personal endpoint in this architecture that the technical literature consistently underspecifies, and it is where the IS contribution becomes sharpest.

Life Atlas — WINNIIO's sovereign interface layer — is that personal endpoint. Your body twin, your calendar, your location, your preferences: a personal digital twin that communicates with the city twin to negotiate your experience of it. Not passively receiving what the network decides to give you — actively participating in what the network optimizes for [1][2].

The mechanism is LPI — the Life Programmable Interface. LPI is a universal consultation layer: every request, from any AI provider, passes through LPI before execution. In the context of a 2035–2040 holographic city, this means your personal guardian agents — running on LPI — are continuously negotiating on your behalf with city-scale infrastructure. When the city twin detects that your neighborhood node is bandwidth-constrained ahead of a major event, your LPI agent negotiates your allocation priority based on your declared context: are you in a holographic medical consultation, a work session, or leisure streaming? The network does not make that determination unilaterally. Your agent makes it for you, within the policy bounds you set, before any provider touches your data.

This creates a new interaction paradigm with direct IS implications. Traditional network management is hierarchical: operator sets policy, infrastructure executes, user receives. The LPI model introduces a peer layer — personal digital twins as first-class participants in resource allocation decisions. Guardian agents manage bandwidth, privacy, and health data with explicit user-defined priorities. The architecture is not operator-to-user but operator-to-city-twin ↔ LPI ↔ user-twin — a bilateral negotiation where the user's sovereign agent has standing in the protocol.

The privacy architecture follows from this. Health data generated by your body twin — biometric context, wellness state, recovery metrics — flows through LPI before any city system can observe it. You decide what the city gets: anonymized load signals for network planning, nothing more, unless you choose otherwise. Privacy is not a policy layer bolted on after the architecture is designed. It is the architecture [1][2].

This is the IS contribution the field is missing: not smarter networks, but networks that are accountable to individuals, through a sovereign consultation layer, as a first-class architectural requirement.

Limitations and Open Questions

It would be intellectually dishonest to describe this trajectory without naming what could prevent or deform it.

Computational cost is the first constraint. A city-scale dynamic twin that updates at sub-second cadence, integrates sensor streams from transport, energy, health, and telecom, and runs continuous optimization against that state is a substantial compute problem. Current estimates for city-scale digital twin infrastructure run to hundreds of petaflops of sustained compute — costs that are declining but not yet at the level required for the smallest municipalities to participate without external subsidy.

The digital divide is the second. Who gets the twin-native city? Tokyo, Seoul, and Singapore will likely achieve synchronized twin coverage by 2040. Rural communities in sub-Saharan Africa, small island nations, and post-conflict regions face a different reality. If twin-native infrastructure becomes the prerequisite for quality of life at the level described here, the gap between twin-enabled and twin-excluded communities becomes a new axis of inequality — potentially more durable than the current connectivity divide because it is harder to bridge with a single infrastructure investment.

Standardization fragmentation is the third. The O-RAN Alliance [8][9], 3GPP, and ITU-T [10] are not fully aligned on interface specifications, and the 6G standardization process is still open. A world where different cities implement incompatible twin protocols is a world where the cross-domain convergence described in this article simply does not happen — because the APIs do not agree on data formats, ontologies, or trust models.

Finally, the surveillance risk of anticipatory networks is real. A network that knows where you are going before you arrive, that models your behavior well enough to pre-position resources for you, is also a network that could be weaponized for population monitoring at a level that makes current surveillance infrastructure look modest. The LPI sovereign layer described above is one architectural answer. But it is an answer that requires regulatory frameworks, technical standards, and social consensus that do not yet exist.

These are not reasons to stop. They are reasons to build the governance structures alongside the technical architecture, not after.

The Difference Between Now and Then

In 2025, a Rakuten engineer in Tokyo monitors dashboards for anomalies, reviews alerts, deploys fixes, and runs drive tests to understand what the network actually looks like to a user. Skilled, dedicated work — and entirely reactive.

In 2040, that same engineer's role is policy definition and boundary setting. They decide what the network is allowed to optimize for, what the acceptable trade-offs are between capacity and energy efficiency, what the minimum performance guarantees are. The optimization itself runs in continuous closed loops, validated against twins, deployed through autonomous systems.

The network has not replaced the engineer. It has changed what the engineer's expertise is for.

The key architectural insight is this: in 2025, the network and the twin are two separate things. The twin is a model of the network. In 2040, that distinction dissolves. The twin is not a model of the network — it is the network's intelligence layer. The network is not separate from its twin — it is the twin's physical substrate. They merge.

A living organism does not have a separate model of its cardiovascular system that it consults to decide whether to raise its heart rate. The intelligence is distributed through the system itself. That is the architecture trajectory for networks.

Why This Matters Beyond Tokyo

Rakuten's Open RAN bet was not just a cost optimization play. It was an architectural statement: the intelligence of a mobile network should not be locked inside proprietary silicon from a single vendor. It should be open, composable, and owned by the operator.

That philosophy scales globally in ways that proprietary stacks cannot.

A rural community in Ukraine, a favela in São Paulo, a remote island network in the Indonesian archipelago — none of these can afford the enterprise pricing of a Nokia or Ericsson full-stack deployment. All of them can run software-defined Open RAN on commodity hardware, with digital twin optimization that was trained on Tokyo's dense urban data and adapted to their specific environment.

The methodology WINNIIO brings — outcome-first, cross-domain, modular, SMILE-structured — was built for this. Not for one city's twin. For a world where every community, at every scale, can participate in the twin-native future on their own terms.

Tokyo in 2040 is not the destination. It is the proof point.

References

[1] Waern, N. (2025). From One Room to Fifty: Orchestrating Explainable AI, Resilience, and Contextual Interoperability in the Built Environment. Zenodo. https://doi.org/10.5281/zenodo.17462962

[2] Waern, N. (2025). Beyond the Shadows — Contextual Awakening, Federated Learning, and the Realization of Reality through Digital Twins. Zenodo. https://doi.org/10.5281/zenodo.17464804

[3] Grieves, M. (2014). Digital twin: Manufacturing excellence through virtual factory replication. White Paper, Florida Institute of Technology.

[4] Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9), 3563–3576.

[5] Kerbl, B., Kopanas, G., Leimkühler, T., & Drettakis, G. (2023). 3D Gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4), 1–14.

[6] Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV). Springer, Cham.

[7] Waern, N. (2026). From Screening to Soul Graph: A Local-First Architecture for Gamified Talent Progression in Agent-Native Organisations. Zenodo. https://doi.org/10.5281/zenodo.19672692

[8] O-RAN Alliance. (2023). O-RAN Working Group 2: AI/ML workflow description and requirements. O-RAN.WG2.AIML-v05.00. O-RAN Alliance.

[9] O-RAN Alliance. (2023). O-RAN Working Group 3: Near-Real-Time RAN Intelligent Controller and E2 interface specifications. O-RAN.WG3.E2GAP-v03.00. O-RAN Alliance.

[10] ITU-T. (2023). ITU-T Focus Group on Technologies for Network 2030: A blueprint of technology, architecture and systems for a 5G-and-beyond packet network. Focus Group Technical Report. International Telecommunication Union.

[11] Nokia Bell Labs. (2022). Nokia Bell Labs 6G white paper: The 6G era of communication networks. Nokia Corporation.

[12] Batty, M. (2018). Inventing Future Cities. MIT Press.

[13] Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford University Press.

[14] Samsung Research. (2020). The next hyper-connected experience for all: Samsung 6G — The next generation of hyperconnected experience. Samsung Electronics.

Nicolas Waern is CEO of WINNIIO AB and Life Atlas. He builds digital twin ecosystems across domains — from biological systems to urban infrastructure — with a focus on sovereignty, modularity, and outcome-first methodology. WINNIIO is actively partnering with network operators, city governments, and research institutions to accelerate the convergence of domain-specific twins into coherent, anticipatory intelligence layers.

Connect: linkedin.com/in/nicolaswaern | winniio.io