A Study on AI-powered Ultra-low Latency in 6G: A Blueprint for the Next-Generation Mobile Communication System
Keywords:
6G, Accuracy, Artificial intelligence, Breach time, Energy, Predictive scheduling, Reinforcement learning, Terahertz communications, Ultra-low latencyAbstract
The advent of 6G promises a paradigm shift from merely faster data rates to a holistic re-engineering of the wireless ecosystem, where ultra-low latency (ULL) becomes a foundational service rather than an aspirational metric. Achieving sub-millisecond round-trip times across heterogeneous, ultra-dense networks demands more than incremental hardware upgrades; it calls for a symbiotic partnership between advanced radio architectures and intelligent control planes powered by artificial intelligence (AI). This paper explores how AI-enabled predictive scheduling, context-aware beamforming, and decentralized reinforcement-learning (RL) agents can pre-emptively mitigate the latency contributors that have traditionally plagued mobile communications-propagation delays, queueing bottlenecks, and protocol overhead. By fusing real-time network telemetry with learned models of traffic dynamics, AI can orchestrate proactive resource allocation, dynamically re-route traffic around transient congestion, and even reshape the physical layer waveform on the fly. Simulation results derived from a realistic multi-cell, terahertz‑band 6G testbed demonstrate that AI-driven latency management can shave 30‑70 % off the tail latency distribution, delivering deterministic sub-500 µs guarantees for mission—critical applications such as tactile internet, autonomous vehicular swarms, and remote surgical robotics. The findings underscore that ultra-low latency in 6G is not just a hardware problem—it is an intelligence problem, and the convergence of AI and next-generation radio access is the key to unlocking truly responsive, immersive mobile experiences.
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