Physical AI isn’t “just more traffic” I Nokia

Source Domain: www.nokia.com

The world is witnessing a new revolution, comparable in scale to the Industrial, Internet, and mobile broadband revolutions. Artificial intelligence has been unleashed by large language models and the rapid growth of applications that depend on them. Most mobile devices already support a wide range of AI-driven applications, such as scene recognition, document generation, text-based chat, voice conversations and image and video generation and editing. These new applications will inevitably affect mobile traffic patterns.

A recent Nokia report analyzing more than 50 AI applications highlights several trends, including a shift toward higher uplink traffic, overall traffic growth, and increasing sensitivity to latency for conversational voice and chat applications. While these impacts are becoming clearer, the more consequential question is whether emerging trends, particularly Physical AI, require a fundamental change in how traffic is handled in the radio access network (RAN).

A brief history of major radio access design changes

Mobile networks are designed to support a wide range of traffic types, from high volume video streaming to low latency voice to low volume messaging. Historically, this versatility has allowed operators to meet new demands primarily by adding capacity. In practice, many advanced quality-of-service (QoS) features remain underutilized, with best-effort delivery serving most traffic, supported by some degree of overprovisioning. In other words, “throwing bandwidth at the problem” has proven remarkably resilient.

There are, however, precedents where capacity scaling alone was not sufficient.

The first came with the shift from circuit-switched voice to packet-based IP traffic. Early mobile systems were optimised for deterministic voice patterns, where similar sized data blocks arrived at regular intervals. IP traffic introduced variable packet sizes and timing, making circuit-style resource allocation…

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