arXiv:2606.03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control. Years on, real 5G Time Division Duplexing (TDD) networks still show median Uplink (UL) round-trip times in the 50-70 ms range, largely because of the Scheduling Request (SR) procedure that a User Equipment (UE) must complete before transmitting UL data. Existing remedies, pr
The paper identifies persistent performance gaps in 5G URLLC, years after its commercial rollout, indicating a fundamental challenge in achieving advertised latency targets.
Improving URLLC performance is critical for unlocking advanced applications in industrial automation, V2X, and unmanned systems, which are foundational for future economic and military competitiveness.
This research proposes a new online-learning approach to dramatically reduce URLLC latency, potentially enabling more reliable and efficient deployment of time-sensitive applications over existing 5G infrastructure.
- · Industrial automation sector
- · Vehicle-To-Everything (V2X) developers
- · Tactical edge networking providers
- · Unmanned system manufacturers
- · Traditional fixed scheduling algorithms
- · Companies reliant on dedicated high-latency networks
- · Developers of applications with strict latency needs but no adaptive scheduling
Significantly reduced latency in 5G networks for critical applications like V2X and industrial control.
Accelerated development and adoption of autonomous and remote-controlled systems due to enhanced network reliability.
Increased economic value extracted from existing 5G infrastructure, potentially shifting competitive advantage in advanced manufacturing and logistics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI