Enhancing OLLA via Exponential Decay for Efficient Link Adaptation in Emerging 6G Traffic

Outer loop link adaptation (OLLA) refines link adaptation by offsetting the channel quality indicator report. However, its slow convergence—the time required for the algorithm to stabilize to a reliable operating point—can pose challenges for emerging sixth generation (6G) services with strict quality of service requirements. In this work, we propose an exponential decay (ED)-based enhanced OLLA (EOLLA) framework that accelerates convergence while preserving stability. The key idea is to exploit instantaneous channel measurements at the user equipment to form an enhanced quality indicator, which drives the EOLLA offset updates at the gNodeB along an ED trajectory for rapid convergence. To prevent these fast adjustments from undermining robustness, EOLLA incorporates long-term system-level a-priori statistics into its design, enabling stable offset evolution under realistic channel dynamics. Closed-form expressions and a practical implementation with inherently low complexity are provided to support real-world deployment. System-level simulations across diverse deployments and traffic models show consistent improvements: moderate gains for enhanced mobile broadband and up to 33% capacity increase for extended reality traffic (characterized with bounded latency and reliability constraints) in indoor hotspot deployment. These results highlight EOLLA as a promising candidate for future 6G systems with demanding performance constraints.