Dominic Schupke’s research while affiliated with Technical University of Munich and other places

The integration of unmanned aerial vehicles (UAVs) into cellular networks presents significant mobility management challenges, primarily due to frequent handovers caused by probabilistic line-of-sight conditions with multiple ground base stations (BSs). To tackle these challenges, reinforcement learning (RL)-based methods, particularly deep Q-networks (DQN), have been employed to optimize handover decisions dynamically. However, a major drawback of these learning-based approaches is their black-box nature, which limits interpretability in the decision-making process. This paper introduces an explainable AI (XAI) framework that incorporates Shapley Additive Explanations (SHAP) to provide deeper insights into how various state parameters influence handover decisions in a DQN-based mobility management system. By quantifying the impact of key features such as reference signal received power (RSRP), reference signal received quality (RSRQ), buffer status, and UAV position, our approach enhances the interpretability and reliability of RL-based handover solutions. To validate and compare our framework, we utilize real-world network performance data collected from UAV flight trials. Simulation results show that our method provides intuitive explanations for policy decisions, effectively bridging the gap between AI-driven models and human decision-makers.

The evolution toward 6G networks introduces unprecedented challenges and opportunities, particularly in the realm of serving both aerial and ground users seamlessly. In this article, we propose a holistic adaptive combined airspace and non-terrestrial network (NTN) architecture designed to address the unique requirements of the 6G era. Three principle features - joint sensing, communication, and computation (JSCC) in three dimensions (3D), cloud-native and artificial intelligence (AI) native, and the flexibility of radio access network (RAN) and core functions of the proposed architecture - are presented. Next, two application scenarios are analyzed: one catering to aerial users and the other supporting ground users, each, in particular, supporting communication links. Finally, we look into the network management and control aspects of the proposed architecture and discuss challenges and future research directions.

Multi-connectivity involves dynamic cluster formation among distributed access points (APs) and coordinated resource allocation from these APs, highlighting the need for efficient mobility management strategies for users with multi-connectivity. In this paper, we propose a novel mobility management scheme for unmanned aerial vehicles (UAVs) that uses dynamic cluster reconfiguration with energy-efficient power allocation in a wireless interference network. Our objective encompasses meeting stringent reliability demands, minimizing joint power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we propose a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework, specifically tailored for dynamic clustering and power allocation. The edge cloud connected with a set of APs through low latency optical back-haul links hosts the high-level agent responsible for the optimal clustering policy, while low-level agents reside in the APs and are responsible for the power allocation policy. To further improve the learning efficiency, we propose a novel action-observation transition-driven learning algorithm that allows the low-level agents to use the action space from the high-level agent as part of the local observation space. This allows the lower-level agents to share partial information about the clustering policy and allocate the power more efficiently. The simulation results demonstrate that our proposed distributed algorithm achieves comparable performance to the centralized algorithm. Additionally, it offers better scalability, as the decision time for clustering and power allocation increases by only 10% when doubling the number of APs, compared to a 90% increase observed with the centralized approach.

Mobility management for terrestrial users is mostly concerned with avoiding radio link failure for the edge users where the cell boundaries are defined. The problem becomes interesting for an aerial user experiencing fragmented coverage in the sky and line-of-sight conditions with multiple ground base stations (BSs). For aerial users, mobility management is not only concerned with avoiding link failures but also avoiding unnecessary handovers while maintaining extended service availability, especially in up-link communication. The line of sight conditions from an Unmanned Aerial Vehicle (UAV) to multiple neighboring BSs make it more prone to frequent handovers, leading to control packet overheads and delays in the communication service. Depending on the use cases, UAVs require a certain level of service availability, which makes their mobility management a critical task. The current mobility robustness optimization (MRO) procedure that adaptively manages handover parameters to avoid unnecessary handovers is optimized only for terrestrial users. It needs to be updated to capture the unique mobility challenges of aerial users. In this work, we propose two approaches to accomplish this: 1) A model based service availability-aware MRO where handover control parameters, such as handover margin and time to trigger are tuned to maintain high service availability with a minimum number of handovers, and, 2) A deep Q-network based model free approach for decreasing unnecessary handovers while maintaining high service availability. Simulation results demonstrate that both the proposed algorithms converge promptly and increase the service availability by more than 40% while the number of handovers is reduced by more than 50% as compared to traditional approaches.

In modern cell-less wireless networks, mobility management is undergoing a significant transformation, transitioning from single-link handover management to a more adaptable multi-connectivity cluster reconfiguration approach, including often conflicting objectives like energy-efficient power allocation and satisfying varying reliability requirements. In this work, we address the challenge of dynamic clustering and power allocation for unmanned aerial vehicle (UAV) communication in wireless interference networks. Our objective encompasses meeting varying reliability demands, minimizing power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we introduce a novel approach based on reinforcement learning using a masked soft actor-critic algorithm, specifically tailored for dynamic clustering and power allocation.