ML-OLLA improves satellite link speed and reduces delays for 5G and 6G
ML-OLLA: Link Adaptation for Real-Time Traffic in 5G/6G NTN Systems
Networking and Internet Architecture
Summary
Connecting people through satellites is important for global internet, but signals can be slow and change quickly, causing delays. The authors present ML-OLLA, a new method that adjusts how data is sent in steps, starting fast and shifting to more reliable settings if needed. This approach helps keep connections fast while reducing errors, even with the long delays satellites introduce. Their tests show this method can support faster communication and lower delays for very reliable connections compared to older methods.
What this means in practice
- •For satellite network engineers: Configure satellite links for real-time 5G/6G traffic to achieve higher capacity and lower latency using ML-OLLA's stepwise adaptation process.
- •For cellular network operators: Improve non-terrestrial network performance for low-latency applications by adopting ML-OLLA for satellites supporting 5G and future 6G services.
Authors
Artur Magamedov, Artem Krasilov, Roman Zlobin, Evgeny Khorov
Abstract
Reliable low-latency connectivity for real-time applications is a key requirement for 5G and future 6G non-terrestrial networks. Low Earth Orbit satellites enable global coverage but impose long propagation delays, rapid channel variations, and delayed feedback, which challenge conventional link adaptation algorithms designed for terrestrial networks. To address this problem, the paper proposes a Multi-Level Outer-Loop Link Adaptation (ML-OLLA) algorithm that assigns a dedicated target block error rate and an independent adaptation process for each transmission attempt. With ML-OLLA, early transmission attempts use higher-rate modulation and coding schemes (MCSs), while later ones use more robust MCSs. The core contribution of this work is a low-complexity data-driven parameter selection method that determines ML-OLLA parameters. Numerical results obtained with ns-3 show that the proposed ML-OLLA algorithm increases the system capacity by up to four times and reduces the minimum supported latency budget. In particular, for the 99.9% reliability target, ML-OLLA reduces the minimum supported latency budget from 60 ms to 40 ms compared with the baseline OLLA.