What problem does it solve?
Mobile data traffic does not grow evenly across a network. Some cells can congest at busy hours while others are rarely loaded, and new housing, offices and events can move demand around faster than annual planning cycles follow. A new site or carrier in the wrong place ties up investment while customers a few streets away still see slow speeds.
Between investments, radio engineers tune thousands of parameters (antenna tilts, power, handover and load balancing settings) to squeeze more out of the existing network. That work is slow and manual. Vodafone describes a trial in which a machine learning algorithm found optimal voice over LTE settings for 450 cells in four hours, a task that would have taken an engineer around two and a half months by hand. With 4G, 5G and several vendors in one network, manual tuning gets harder still.
How does it work?
- Forecast demand. Models forecast traffic per cell and area from history, subscriber growth, device mix and planned developments, and flag where congestion will appear.
- Estimate capacity. The system estimates how much more traffic each cell can carry with its current configuration and where the limit is (spectrum, hardware, backhaul, interference).
- Optimise before building. Self optimizing network functions tune parameters such as tilt, power and load balancing so neighbouring cells share load. They can act ahead of demand: in a Vodafone trial in Ireland, algorithms predicted where 3G traffic would peak in the next hour so the network could rebalance load in advance.
- Recommend investments. Where optimisation is not enough, the system simulates candidate sites, carriers or hardware upgrades and ranks them by traffic served per unit of spend.
- Close the loop. Planners approve investments; after each change the system compares the measured effect with its forecast and recalibrates.
- Audience
- Employee facing
- Autonomy
- Supervised agent
- Adoption
- Early adopters
- Channels
- Internal tools, API and system to system
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Cycle time reduction | Too few to pool | at least 95% | 1 | 1 organization |
Value drivers: Lower cost to serve, Customer experience, Employee productivity, Speed and cycle time.
Indicative value
A mobile operator with a capacity driven radio investment budget of USD 200 million a year
USD 4 million to USD 12 million
Capacity investment avoided or deferred per year
How this is calculated
Formula: capacityCapex * capexEfficiency. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Annual capacity driven radio investment capacityCapex, USD per year | 200,000,000 | 200,000,000 | The reference operator. |
| Share of capacity investment avoided or deferred through better targeting and optimisation capexEfficiency, fraction of capacity investment | 0.02 | 0.06 | Editorial assumption. None of the evidence on this page publishes a verified investment saving; replace with your own post investment reviews. |
What it leaves out: Investment only, and deferral is not the same as saving. It leaves out engineering time released, the revenue and churn effect of fewer congested cells, and the cost of the planning platform and data.
Who already uses it?
5 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Deutsche Telekom
Germany · Telecommunications · 2025
Deutsche Telekom's RAN Guardian Agent, built with Gemini models on Google Cloud, went live in its German mobile network in November 2025. It is a multi agent system: one agent finds upcoming public events from public sources, another assesses whether nearby cells can carry the expected traffic and monitors them live, and a third executes corrective actions such as reallocating resources or adjusting configuration, documenting every action. It is being extended to the Czech Republic and Croatia. In February 2026 Deutsche Telekom announced MINDR, which applies the same approach end to end across radio, transport and core domains, with first production releases planned for later in 2026.
- Cycle time reduction: at least 95%, live operations, major events
"And in live operations it has reduced the time needed to manage major events from hours to around a minute, a more than 95% improvement."
Claimed by: organization - Interactions handled: at least 100, first month after launch, Christmas market events
"Since its launch in November 2025, RAN Guardian Agent has autonomously triggered over 100 remediation actions at Christmas market events during its first month."
Claimed by: organization
Vodafone
United Kingdom · Telecommunications · 2017
Vodafone ran early machine learning trials in centralised self organizing networks. In Germany, with Huawei, an algorithm found the optimal voice over LTE settings for 450 randomly chosen cells in four hours, a task Vodafone says would take an engineer around two and a half months. In Ireland, with Cisco, algorithms predicted where 3G traffic would peak in the following hour so the network could rebalance load between neighbouring cells; Vodafone reports an average 6 percent improvement in mobile download speed in initial results. Vodafone planned commercial use from its 2018/19 financial year.
No outcome disclosed.
Telefónica España
Spain · Telecommunications · 2025
Telefónica España built a network data platform on Microsoft Azure (Azure Data Explorer, Azure Databricks and Power BI) to store and analyse the large volumes of data its 4G and 5G mobile network produces. The team uses it for anomaly detection, to address issues before they affect customers, and for automated network optimization. Telefónica says the project is live with several use cases deployed and that results have been very positive; Microsoft's summary adds substantial savings in operating costs. No figures are published.
No outcome disclosed.
stc Group
Saudi Arabia · Telecommunications · 2024
Nokia deployed its MantaRay Cognitive SON, an AI powered feature of its self organizing network platform, in stc's commercial network in Saudi Arabia for the first time. The system optimises radio parameters autonomously. Nokia reports that during a period of high traffic it processed more than 10,000 actions, raised the utilisation rate of loaded cells by about 30 percent and average user throughput by 10 percent while traffic rose 40 percent, and that it reduced manual work. The results are stated by the vendor.
No outcome disclosed.
NTT DOCOMO
Japan · Telecommunications · 2022
NTT DOCOMO deployed Nokia's AI radio frequency capacity planning software, customised to its requirements, to support its 5G rollout. The software predicts the capacity of 4G cells from base station performance data and simulates the best candidate locations for 5G cells and radio hardware to meet the capacity needed in an area, helping DOCOMO see where congestion is starting and where to plan upgrades. No results are published.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Traffic, quality and utilisation counters per cell and carrier, with at least a year of history
- Site, antenna and configuration inventory that matches the live network
- Subscriber and device growth data by area
- Planned developments, events and competitor coverage where available
Systems to integrate
- Radio network management and configuration per vendor
- Self organizing network (SON) platform
- Planning and propagation tools
- Network data lake or analytics platform
- Capital planning and project tracking systems
Complexity: High
Forecasting and optimisation need clean, granular performance data from every vendor and a trusted digital view of the network. Automated parameter changes touch live customers, so they need strong guardrails and radio engineering buy in.
- 1
Clean the network view
Reconcile inventory with the live configuration. Forecasts and simulations on a wrong site database lead to wrong investments.
- 2
Start with forecasting and ranking
Use models to rank congested and soon to be congested cells, and let planners compare the ranking with their own judgement for a planning cycle.
- 3
Automate reversible optimisation
Let SON functions change parameters within bounds and with automatic rollback, starting in one cluster with a control area.
- 4
Link to the investment process
Feed the ranked recommendations into capital planning, and review every investment afterwards against the forecast it was based on.
- 5
Extend across vendors and technologies
Move from single vendor tools to a view that covers 4G, 5G and every vendor, so optimisation in one layer does not hurt another.
Guardrails
- Parameter changes only within engineering defined bounds, with automatic rollback on quality loss
- Investments above a set value always approved by planners and finance
- Exclusion of critical sites and emergency coverage from automated changes
- Every automated change logged with its reason and measured effect
KPIs to instrument
- Share of congested cells, by hour and area
- Forecast accuracy of traffic and congestion per planning cycle
- Throughput and quality before and after each optimisation or investment
- Engineering hours per optimisation task
- Capacity investment per unit of traffic carried
Human in the loop
Radio planners and engineers own investment decisions and the bounds for automated optimisation. They review recommendations each planning cycle, approve changes outside the bounds, and use post investment reviews to decide how much to trust the forecasts.
Common failure modes
- Optimising the average, hurting the edge
- Changes improve cell averages while users at cell edges or indoors lose service. Monitor distributions, not only averages.
- Forecasts built on the past only
- Models miss new housing, venues or competitor moves. Add planners' local knowledge and external data.
- Oscillating parameters
- Several automated functions fight each other and parameters flip back and forth. Coordinate SON functions and damp changes.
- Trusting a vendor's black box
- Recommendations cannot be explained to finance or engineers. Require the reasoning and data behind each recommendation.
What are the risks and rules?
EU AI Act
Depends on design
Forecasting demand, ranking congested cells and recommending investments is normally minimal risk. Under Article 6(2), Annex III point 2 lists AI systems intended as safety components in the management and operation of critical digital infrastructure as high risk, and Recital 55 ties this to the digital infrastructure in the Annex to Directive (EU) 2022/2557, which includes providers of public electronic communications networks. Recital 55 defines such safety components as systems that directly protect the physical integrity of the infrastructure or the health and safety of persons and property and that are not necessary for the system to function. Closed loop parameter optimisation on the live radio network is high risk only when it serves in that role, for example a loop whose purpose is to protect emergency call availability, so each automated loop should be assessed against point 2 and the outcome documented. Loops that only optimise performance or capacity are usually not safety components.
Guidance
- Annex III, high risk AI systems referred to in Article 6(2) (European Union, Europe). Point 2 is the relevant test if automated optimisation becomes a safety component of network operation.
- Recital 55, safety components of critical infrastructure (European Union, Europe). Explains which critical digital infrastructure is meant and what counts as a safety component, and excludes components used solely for cybersecurity.
Controls to put in place
- Documented bounds for automated parameter changes with an accountable owner
- Post investment review comparing forecast and measured traffic
- Audit log of automated configuration changes
- Model validation of forecasts before each planning cycle
Frequently asked questions
- What does AI add to self optimizing networks?
- Classic SON functions apply rules that engineers set. AI based SON uses models to choose and time parameter changes autonomously. Nokia reported in 2024 that its MantaRay Cognitive SON, deployed in stc's commercial network in Saudi Arabia, processed more than 10,000 actions in a high traffic period and raised the utilisation of loaded cells by about 30 percent.
- Can AI decide where to build new sites?
- It can rank candidate locations. In 2022 Nokia deployed its AI capacity planning software for NTT DOCOMO to predict the capacity of 4G cells and simulate the best candidate locations for 5G cells and radio hardware, and DOCOMO said it expected the software to help its network capacity design work. The investment decision should stay with planners and finance, who can weigh cost, coverage and local knowledge.
- How fast does machine learning tune a network compared with engineers?
- In a Vodafone Germany trial with Huawei, a machine learning algorithm found optimal voice over LTE settings for 450 cells in four hours, which Vodafone says would have taken an engineer around two and a half months.
How to cite this page
Blits.ai AI Use Case Library, "AI for mobile network planning and capacity optimization", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/network-planning-and-capacity-optimization. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published