Five g one-way delay model improves understanding of network queues
One-Way Delay Model for 5G Low Latency, High Data Rate Communications
Networking and Internet Architecture
Summary
Fast and smooth 5G internet needs keeping data from piling up in network queues, especially at slow points like the wireless link. The authors show that measuring the waiting time of data packets, called one-way delay, helps understand when queues build up, but it’s hard to measure directly. They break down this delay into parts that can be measured or estimated at the user device and via network info, allowing a better guess of queue status. This helps improve ways to control data flow and avoid network slowdowns.
What this means in practice
- •For network schedulers: Detect bottleneck queues more accurately in 5G to improve scheduling decisions and maintain low latency during high data rates.
- •For cellular network operators: Implement improved congestion control methods that use decomposed delay measures to prevent underutilization of 5G networks.
Authors
Hamid Hassani, Sonia M. Heemstra de Groot, Ignas G. Niemegeers, Kishor C. Joshi, George Exarchakos
Abstract
Achieving low delay and high data rate communication in 5G requires preventing persistent queue backlogs at path bottlenecks. A key indicator of queue buildup is an increase in one-way delay (OWD) of packets. However, accurately measuring OWD is challenging due to synchronization issues between the sender and receiver. Moreover, using OWD to infer the onset of queueing can be inefficient. For instance, congestion control mechanisms that rely on latency as a congestion signal may result in cellular network underutilization, see, e.g., [1]. Thus, to develop more efficient rate control approaches, it is crucial not only to estimate OWD accurately but also to perform a more fine-grained analysis that provides detailed insights into the queue status. This paper presents a framework for modeling OWD in 5G edge networks, where the wireless link is typically the path bottleneck. %, by modeling it through measurable delay components. We demonstrate that OWD is dominated by queueing delay (QD), particularly at high data rates. We analyze QD by decomposing it into two main components: aggregation delay and channel access time. Aggregation delay can be accurately measured at the user equipment (UE), and channel access time can be estimated based on network conditions. Our key contribution shows that QD, while challenging to measure directly at the base station (BS), can be closely approximated by combining these components. We validate this model through NS3 simulations under varying network loads and single- and multi-UE scenarios. This framework provides a foundation for developing advanced mechanisms for congestion control and bottleneck detection in 5G networks.