AI glossary · Advanced · Efficiency & infra · AI platforms & solutions · Telecom
AI-RAN
AI-RAN is a telecom network design approach that deeply embeds AI into the radio access network (RAN), which connects mobile phones to base stations, to make network operations smarter with AI and to run AI services on the same base station computing resources.
About this term
- Level
- Advanced · Specialist terms that need the inner workings
- Category
- Industry AI
- Industries
- Telecom
- Added
- 2026-10-10
How it works
A RAN consists of base station antennas and the equipment behind them that processes radio signals, traditionally built on dedicated telecom hardware. AI-RAN moves this processing to software running on general-purpose servers and accelerators such as GPUs, and layers AI models on top. It is commonly described in three parts. The first is using AI to improve the RAN itself (AI for RAN), handling tasks such as predicting radio conditions, adjusting beam direction and saving energy. The second is having telecom processing and AI workloads share the same computing resources (AI and RAN), and the third is running AI services close to users at the network edge (AI on RAN).
Three parts of AI-RAN
AI for RANImproving the network with AI
- Predicting radio conditions
- Adjusting beam direction
- Saving energy
AI and RANSharing resources
- Telecom and AI processing on the same server
- Spare capacity for AI workloads
AI on RANAI services on the network
- Inference close to users
- Low-latency services
공통 All are built on software base stations running on general-purpose servers and GPUs
Why it matters
Base stations account for a large share of carriers' capital spending, but much of their processing capacity sits idle during low-traffic hours. AI-RAN is an attempt to raise equipment utilization by putting this spare capacity toward AI inference. Also, as the number of mobile users grows and services diversify, it has become hard to run networks optimally with human-defined rules alone, and having AI learn conditions and adjust in real time has emerged as an alternative. In discussions of next-generation mobile communications (6G), an 'AI-native' direction that builds AI into network design from the start is also frequently raised.
Things to know
Telecom networks are national infrastructure, so a wrong AI decision can lead to dropped calls or service outages across wide areas. For this reason, rather than applying AI decisions immediately, it is common to validate them first in simulations or digital twins and to keep a safeguard that reverts to existing rules if problems arise. Spectrum use and network stability are subject to each country's telecom regulations, and the location and usage data handled by the network is personal information, so de-identification and access control are needed when it is used for training. General-purpose GPU servers can consume more power than dedicated equipment, so whether the cost and energy efficiency actually work out is still something each deployment has to assess.
Example
With AI-RAN, carriers can automatically put some antennas to sleep at night in areas with few users to save power, and in places where crowds suddenly gather, such as stadiums, have AI predict congestion and allocate resources in advance. Operators are also testing ways to use the same base station servers during spare time to handle low-latency AI services, such as vision inspection for nearby factories or AI assistants for smartphones, as a new source of revenue.