PhD Defense: “Predictability, Prediction, and Control of Latency in 5G and Beyond: From Theoretical to Data-Driven Approaches”

TECoSA PhD student Samie Mostafavi will defend his thesis at Hybrid, Room D3, Lindstedtsvägen 9, KTH Campus and on zoom, via this link.  Contact James Gross (jamesgr@kth.se) if you are interested in attending.

Abstract: The explosive growth of mobile communication and the proliferation of real-time applications, such as industrial automation and extended reality (XR), have created unprecedented demands for ultra-reliable low-latency communication (URLLC) in wireless networks. For example, in industrial closed-loop control systems, data must be transmitted within a target delay of at most a few milliseconds; violations can lead to costly failures and, therefore, must occur with probabilities below 0.0001 (or, reliability above 0.9999).

This dissertation addresses the critical challenge of end-to-end latency prediction and control in these dynamic and stochastic environments, bridging the gapbetween the inherent randomness of wireless communication and the deterministic performance guarantees required by time-sensitive applications. In this thesis, we adopt a twofold approach, combining rigorous theoretical analysis with practical, data-driven methodologies. First, we introduce a framework for analyzing predictability that quantifies the inherent limits of latency forecasting in communication networks. Through analysis of Markovian systems, including single-hop and multi-hop queues, exact expressions and spectral-based upper bounds for predictability are derived, revealing the crucial influence of network topology, state transitions, and observation defects.

Building on this foundation, we developed and implemented data-driven techniques for probabilistic delay prediction. A key contribution is a tail-optimized prediction method that integrates Extreme Value Theory (EVT) within a mixture density network framework, significantly enhancing the accuracy of predicting rare, high-latency events critical for URLLC. To demonstrate the practical utility of these predictions, ”Delta,” a novel active queue management scheme, is introduced. Delta integrates real-time delay violation probability predictions into packet-dropping decisions, dynamically adapting to delay variations and significantly reducing delay violations.

To validate these approaches, the ExPECA testbed and EDAF framework were developed, enabling fine-grained delay measurement and decomposition in real 5G systems. Extensive experiments on both commercial off-the-shelf 5G and software-defined radio-based OpenAirInterface platforms confirmed the superior accuracy and efficiency of the proposed EVT-enhanced models. Furthermore, temporal prediction models, leveraging LSTM and Transformer architectures, were developed and shown to achieve higher accuracy compared to the baseline approaches in real 5G network experiments, capturing the time-varying dynamics of wireless networks and providing accurate multi-step forecasts.

This dissertation advances latency prediction and control for wireless networks, offering both theoretical foundations and practical solutions for time-sensitive applications. These findings have significant implications for designing and operating next-generation wireless networks, paving the way for more dependable communication. Future work should focus on integrating these prediction models to optimize the network and extending the framework to encompass broader quality of service metrics and emerging wireless technologies.

Details of the panel are shown below:

Respondent: Samie Mostafavi
Opponent: Roberto Verdone, University of Bologna
Committee: Viktoria Fodor, KTH; Holger Rootzen, Chalmers; Serveh Shalmashi, Ericsson Research; (stand-in Mats Bengtsson, KTH)
Chair: Mikael Skolgund
Supervisors: James Gross, György Dan