Terminology » AI for RAN vs AI on RAN vs AI & RAN: Differences

AI for RAN vs AI on RAN vs AI & RAN: Differences

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As mobile networks evolve from 5G Advanced (3GPP Release 18/19) into the 6G era, Artificial Intelligence (AI) and Machine Learning (ML) are no longer just buzzwords; they are fundamental components of the network architecture. In telecom industry, the intersection of AI and the Radio Access Network (RAN) is generally categorized into three distinct paradigms: AI for RAN, AI on RAN and AI and RAN. Let us understand each concept, followed by their key differences.

AI for RAN (AI as the Optimizer)

This refers to applying AI/ML algorithms to optimize, manage, and operate the wireless network itself. In this scenario, the RAN is the subject that is being improved by AI.

Telecom networks generate massive amounts of data (traffic loads, signal interference, user mobility). AI for RAN ingests this data to automate complex decisions that are too fast or complicated for human engineers or traditional rule-based algorithms.

Examples:

  • Energy Savings : AI predicting when cell traffic will drop and putting specific antenna elements to sleep to save power without dropping calls.
  • Mobility Optimization: Predicting a user’s trajectory in a moving car and seamlessly handing them off to the next cell tower before the signal degrades.
  • O-RAN RIC: The Open RAN Intelligent Controller (RIC) is the ultimate example of “AI for RAN,” hosting AI applications (xApps/rApps) to manage radio resources in near real time.

AI on RAN (AI as the Payload/Service)

Also referred to as “AI over RAN,” this treats the network infrastructure as a distributed computing platform to host and process AI workloads for end users or third party applications. Here, the RAN is the host.

Instead of a smartphone sending data all the way to a centralized cloud (like AWS or Azure) to run an AI model—which takes time and bandwidth; the AI inference is executed right at the cell tower using Multi-access Edge Computing (MEC).

Examples in 5G/6G:

  • Autonomous Vehicles
  • Smart Factories
  • Distributed AI: A smartphone and the RAN edge server split the processing of a heavy Generative AI model to save the phone’s battery.

AI and RAN (AI-Native Air Interface)

This represents the ultimate convergence and co-design of AI and wireless communications. Instead of AI sitting “on top” of the network as a management tool, AI is baked directly into the fundamental physics and signal processing of the network. This is the defining characteristic of 6G “AI-Native” networks.

Traditional cellular networks use strict mathematical formulas to process radio signals (modulation, channel coding, error correction). In “AI and RAN,” these rigid mathematical blocks are replaced by Deep Neural Networks.

Examples:

  • Neural Receivers
  • Semantic Communication
  • Joint Communication and Sensing (JCAS)

Key differences

FeatureAI for RANAI on RANAI and RAN
Primary GoalOptimize network performance, automate operations, and reduce costs.Provide low latency AI processing services to users and edge devices.Fundamentally redesign how radio signals are transmitted and received.
Target BeneficiaryThe Mobile Network Operator (MNO).The End User / Enterprise Customer.Both (Massive leaps in spectral efficiency and new capabilities).
LocationIn the network management layers (e.g. Core, O-RAN RIC, NMS).In the Edge Computing servers (MEC) co-located with base stationsDeep inside the physical (PHY) and MAC layers of the radio chips/modems.
Generational FocusMature in 5G Advanced (3GPP Rel-18/19).Growing now with 5G Edge; expanding heavily in 6G.The foundational pillar of 6G research and development.
Key Metric of SuccessEnergy efficiency, reduced dropped calls, balanced traffic loads.Ultra-low latency, high throughput for user apps, battery savings for UEs.Extreme spectral efficiency, resilience to interference, sensing capabilities.

Summary

To understand the distinction, think of the network as a vehicle.

  • AI for RAN is like the mechanic tuning the engine to make it run efficiently.
  • AI on RAN is like the passenger utilizing the vehicle to get to their destination faster.
  • AI and RAN is like replacing the traditional engine with a brand new AI-powered engine.