The path to ‘Mobile AI’ laid out in GSMA report

The global digital economy is increasingly driven by the convergence of advanced mobile communications and artificial intelligence, according to a paper published by the GSMA and GTI Telecom [PDF].

The report states that as 5G scales globally, mobile networks will expand coverage and quality of service, while AI will move from cloud to on-device and the edge. Pervasive mobile connectivity enables widespread access to AI, while AI simultaneously reshapes network architecture.

The end-game is termed “Mobile AI”, which relies on a collaborative device–edge–network–cloud system that combines network reliability and low latency with AI algorithms capable of perception and decision-making. Structure is described as a “three-layer, four-dimension[al]”: vertically linking foundation, execution, and application layers, and horizontally integrating four domains: AI for Network, Network for AI, Mobile AI agents/terminals, and Mobile AI applications.

The whitepaper frames Mobile AI as requiring global cooperation and shared standards, and anticipates that as 5G-Advanced and 6G mature, mobile networks and AI will become a foundation for large-scale intelligent services.

The report in detail

The report’s central claim is that Mobile AI will be built around the interaction between devices, networks, edge computing, and cloud platforms, with mobile infrastructure carrying traffic and supporting AI workloads.

Mobile traffic associated with AI services is expected to grow, and AI-related network traffic is forecast to increase at a CAGR of over 70% over the next decade. Around 2031, AI traffic may exceed traditional application traffic in global networks, the authors claim. The growth in demand for edge AI processing will accelerate, since edge inference systems rely on device-to-network communication, thus placing new requirements for uplink capacity and close-to-zero latency.

The Mobile AI era

The report’s proposed architecture for Mobile AI is summarised as a “device–edge–network–cloud” system. Devices perform local sensing, edge infrastructure handles low latency computation, and clouds provide initial training and on-going reasoning. The telecoms network links these layers, managing traffic and service quality. The telecoms network itself may have to be at least partially-optimised by its own AI instances.

The paper’s three layers and four functional dimensions of Mobile AI are composed of:

• a foundation layer of connectivity, computing resources, and data infrastructure,
• the execution layer packaging the above into deployable services,
• the application layers deliver sector-specific solutions.

The functional dimensions are described as:

• AI applied to network operations,
• networks capable of supporting AI workloads,
• AI-capable devices and agents,
• application ecosystems built on the above abilities.

A large part of the report focuses on AI acting on the networks themselves. AI can assist network planning and operational optimisation, using real time data to adapt capacity and configuration. Similarly, in operations and maintenance, AI systems can identify anomalies, predict faults, and to some extent, coordinate responses. The report argues that these abilities will support the end-game of fully-autonomous network management.

Networks supporting AI applications

Intelligent devices and agents will generate new patterns and high quantities of traffic, and different service requirements. Some applications require low latency for control (robotics or remote operation), while others are based on the use of data-rich sources such as video and sensor streams. Networks will need more flexible service models than traditional best-effort, one-size-fits-all workloads connectivity.

To achieve the new service models, the report suggests three areas in which operators need to adapt:

• improved uplink capacity
• differentiated service quality
• coordination between manufacturers, telecoms operators, and software developers..

Device and edge mobile

Mobile AI devices and agents are presented as major source of demand in the next decade. Smartphones, wearables, robots, and industrial terminals will change from the majority being passive endpoints to become intelligent systems capable of reasoning and executing tasks. At present, devices operate on a hybrid model of local models performing immediate tasks and more complex reasoning occurring at the edge or in the cloud. Connectivity, therefore, becomes part of the IT topology.

There are a range of industry specifics where the Mobile AI paradigm applies especially, such as industrial automation, smart manufacturing, connected vehicles, urban management, healthcare monitoring, and energy. In these environments, AI functions have to operate close to physical installations and rely on networks to provide connections to remote computing to achieve their full operational potential. The next generation of device–edge–network–cloud architecture could provide just that.

Problems and solutions ahead

Infrastructure constraints are presented as a major roadblock, as AI services require greater uplink capacity, lower latency, and high reliability, which current network architectures are not designed for. Mid-band and millimetre wave capacity will influence whether networks can meet these more stringent requirements. Operators will be likely to see rising CAPEX to expand and optimise edge computing infrastructure.

Another challenge concerns standards and interoperability. Fragmentation in agent protocols and AI interfaces has already begun, a trend that should be reversed. Without common standards, integration costs will remain high and services that attempt span different vendors or markets will hit cost barriers. The authors call for stronger collaboration between involved parties via international standards bodies and industry alliances.

There is still a high degree of commercial uncertainty in the form of how to convert the traffic growth associated with AI into revenue. Mobile AI, the paper states, could present new sources of revenue, including the provision of AI infrastructure services, creating data products specific to different sectors, and AI applications for companies’ existing enterprise customers. However, these business models are still very much nascent, and depend the aforementioned cooperation between operators, AI companies, middleware and software vendors, and device manufacturers.

The ideal for Mobile AI

AI workloads, intelligent devices, and distributed computing will reshape network architecture and operators’ business models, but the pace of change will depend on infrastructure investment, spectrum policy, collaboration, and the development of viable commercial services as network traffic increases. Operators that integrate connectivity, edge computing, and data processing capabilities could capture a larger share of the value that AI-driven digital services are said to produce.

(Image source: “Telegraph Poles” by spratmackrel is licensed under CC BY-NC-SA 2.0.)

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