Osel Lhamo

May 10, 2024 | fis.tu-dresden.de | Technische Universität Dresden |Osel Lhamo |Tung V. Doan |Frank H.P. Fitzek

Emerging use cases, such as Tactile Internet, typically demand high reliability and low-latency communication. To fulfill these stringent requirements, 5G networks employ re-transmission within the Radio Access Network (RAN) in the event of packet loss, but at the expense of increased latency. An alternative approach to address this challenge involves leveraging Random Linear Network Coding (RLNC) to recover lost packets, thereby eliminating the necessity for retransmission within the RAN.

Nov 9, 2023 | fis.tu-dresden.de | TUD Dresden |Osel Lhamo |Deutsche Telekom Chair |Tung V. Doan

Retransmission at the Radio Link Control (RLC) layer is the de-facto technique to overcome packet loss at the cost of increased transmission delay in 5G systems. Softwarization of the 5G core and its adoption of cloud-native architecture allows for bringing in more advanced error correction techniques, such as Random Linear Network Coding (RLNC), into 5G systems. RLNC reduces delay and packet loss by proactively generating and sending combinations of original packets at the cost of more computing.

Jul 7, 2023 | fis.tu-dresden.de | Deutsche Telekom Chair |Osel Lhamo |Johannes Busch |Christian L. Vielhaus

Machine learning (ML) equips next-generation networks with anticipatory capabilities. End-to-end predictive Quality of Service (pQoS) leverages ML models to estimate QoS indicators. In this paper, we present several ML models that can estimate the maximum achievable instantaneous throughput (link capacity) of cellular networks. The models do not only estimate the most likely value, but also quantify the uncertainty of their own estimate by providing estimated quantile values as uncertainty bounds.