According to Nokia, AI-generated traffic in most mobile networks is at an early stage, with application maturity and adoption by consumers and enterprises only at the start of a broader AI super cycle.  The Finland based company analyzed more than 50 AI applications and came to three conclusions: higher uplink traffic, overall data growth and increasing sensitivity to delay in conversational services such as chat and voice. Also, the mobile network industry is moving toward “AI-RAN” or “6G-native” structures that embed AI into the network, transforming radio sites into “robotic” nodes capable of edge inference and handling these new demands.

–>Do those findings require a structural change in Radio Access Network (RAN) design?  Let’s take a fresh look…..

Mobile networks traditionally support a heterogeneous mix of traffic, ranging from high-throughput video streaming to low-bandwidth, delay-tolerant messaging. Network operators typically address escalating capacity demands through infrastructure expansion and overprovisioning, relying on best-effort delivery—a model that has proven remarkably resilient. However, capacity alone is insufficient for new use cases.

The transition from circuit-switched voice to packet-switched (voice/video/data) IP traffic requires a redesign to accommodate variable packet sizes instead of predictable, continuous voice patterns. The proliferation of Internet of Things (IoT) devices introduced requirements for massive machine-type communications (mMTC), driving the development of LTE-M and NB-IoT to optimize for deep indoor penetration and power efficiency.  Conversely, consumer web-based services and video streaming scale seamlessly by adding RAN and core capacity. Existing AI applications, such as generative AI chatbots, follow this model, making current RAN architectures adequate for the present load.

A paradigm shift is emerging with Physical AI [1.], which enables machines like autonomous vehicles and robots to interact with the environment in real time. Unlike traditional video streaming, these applications cannot leverage buffering to absorb network jitter. In Physical AI, high-definition video frames and sensor data must arrive within stringent time-to-live (TTL) constraints to remain actionable. This shifts the focus from average throughput to consistent low latency. Maintaining this strict QoS, particularly in the uplink, requires abandoning best-effort, overprovisioned models in favor of guaranteed scheduling, which necessitates substantial reserved capacity or specialized AI-RAN functionalities.

Note 1. Physical AI combines sensors, perception, decision-making, and actuators so machines can understand their environment and take physical (real world) action. Physical AI is used by robots, vehicles, drones, industrial machines, and smart infrastructure that generate and consume real-time sensor, video, and control traffic. These systems need tight coupling between low latency, high reliability, and continuous feedback loops because decisions in software immediately affect physical motion or control. Physical AI is different from typical generative AI because the output is not text or images; it is real-world action. That makes network performance critical, especially for uplink-heavy, latency-sensitive traffic where delays can affect safety, control accuracy, and operational efficiency.

“Physical AI introduces the possibility that large-volume uplink video with strict latency requirements. It will become a meaningful part of mobile traffic, creating both a design challenge and a monetization opportunity,” says Harish Viswanathan, Head of the Radio Systems Research Group at Nokia.

Image Credit: Techslang

Delivering uplink video with sub‑20 ms end-to-end latency can require provisioning three to four times the average uplink capacity. While this level of redundancy is manageable for low-bandwidth services such as voice or control signaling, it becomes prohibitively expensive when supporting high-throughput video streams.

As device densities increase, the required headroom for reserved capacity grows disproportionately, significantly constraining network scalability and driving up cost per bit. This makes Physical AI traffic—characterized by real-time sensor and video inputs for machine analysis—fundamentally different from conventional services, and unsuited to existing best‑effort transport models.  From a Nokia blog post:

“Physical AI will rely on low latency videos to enable real-time control. While the machines or robots will perform most functions locally, there will be situations where they need to rely on more powerful models or human operators to provide remote control via the network. For example, driverless taxis may require remote assistance in unexpected scenarios; service robots may need guidance in complex environments; drones may depend on real‑time video analysis at the point of delivery; and field workers using AR may require timely visual instructions. In all these cases, the network must deliver fresh video information with low and predictable latency.”

To address these challenges, telecom operators are expected to adopt a multi‑layer approach encompassing network architecture, traffic management, and service monetization.

At the Application layer, not all traffic requires identical latency treatment. When video or sensor data is processed by AI rather than consumed by humans, only semantically relevant information may need immediate uplink transmission. This emerging paradigm, known as semantic communication, allows for significant data reduction while preserving information integrity within latency‑critical loops.

Within the network domain, established mechanisms such as Quality of Service (QoS) and network slicing remain essential. QoS enables prioritization of specific traffic classes, while slicing supports logically isolated virtual networks with guaranteed service-level attributes—latency, jitter, bandwidth, and reliability.

At the service and business model level, supporting low-latency, bandwidth-intensive applications reshapes network economics. Operators must evolve beyond best‑effort pricing structures toward differentiated service tiers or performance-based charging models aligned with enterprise and industrial use cases.

For the RAN, Physical AI underscores the need for greater programmability and elasticity. Future RAN designs will depend on dynamic resource allocation, real-time traffic classification, and AI-driven orchestration to balance throughput, latency, and reliability at scale.

As Physical AI deployments expand—from autonomous mobility to precision manufacturing and tele‑robotics—managing high‑volume, low‑latency uplink traffic will become a defining capability for next‑generation network strategy and differentiation. Unlike conventional mobile data, Physical AI cannot rely on buffering to manage traffic spikes. The requirement for continuous video and sensor data to arrive within strict time limits to inform real-time actions makes traditional “best-effort” network approaches inefficient and costly.

• Uplink-Centric Demand: Physical AI shifts the network requirement from downlink-heavy (human consumption) to uplink-heavy (machine-generated) traffic.
• Strict Latency & Throughput: Maintaining consistent low latency (e.g., around 20 milliseconds) for high-volume video uploads can require 3x to 4x more capacity than average, making overprovisioning unsustainable.
• Need for Programmable Architectures: To support this, RAN must move toward more flexible, AI-native architectures that prioritize critical data and provide deterministic, rather than best-effort, performance.
• Semantic Communication: To reduce data volume while maintaining performance, the RAN will need to adopt semantic communication—transmitting only the essential data needed for the AI to make decisions.

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References:

https://www.nokia.com/asset/215147/

https://www.nokia.com/blog/physical-ai-redefining-ran-and-telco-monetization/

https://telcomagazine.com/news/nokia-report-points-to-ai-driven-shift-in-mobile-traffic

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