A. Asadi
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Static Kubernetes manifest validation mainly focuses on syntax and best practices. Many deployment failures are caused by syntactically valid resource definitions that prevent communication between dependent micro-services. This thesis investigates to how static analysis can detect such internal routing misconfigurations in Kubernetes manifests. Moreover, it also investigates the impact of misconfiguration detection by adding dependency context to the analysis. First, the Kubernetes resource model is reduced to a minimal set of resources and attributes that are required to reason about internal reachability. Next, runtime reachability issues are identified that are accepted by Kubernetes and remain undetected by existing validation tools. Based on these findings, the prototype static analysis tool KubeReach is developed, followed by KubeReach+, which extends the analysis with explicit dependency context of the micro-service architecture. The results show that Kubernetes manifests already contain sufficient information to detect several internal routing misconfigurations, but not enough to verify whether the the micro-services can reach its dependencies. Adding dependency context enables the validation of expected communication paths and improves the detection of routing issues that cannot be detected from the Kubernetes manifest alone. This makes dependency-aware static analysis a valuable addition to Kubernetes manifest integritiy.
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Static Kubernetes manifest validation mainly focuses on syntax and best practices. Many deployment failures are caused by syntactically valid resource definitions that prevent communication between dependent micro-services. This thesis investigates to how static analysis can detect such internal routing misconfigurations in Kubernetes manifests. Moreover, it also investigates the impact of misconfiguration detection by adding dependency context to the analysis. First, the Kubernetes resource model is reduced to a minimal set of resources and attributes that are required to reason about internal reachability. Next, runtime reachability issues are identified that are accepted by Kubernetes and remain undetected by existing validation tools. Based on these findings, the prototype static analysis tool KubeReach is developed, followed by KubeReach+, which extends the analysis with explicit dependency context of the micro-service architecture. The results show that Kubernetes manifests already contain sufficient information to detect several internal routing misconfigurations, but not enough to verify whether the the micro-services can reach its dependencies. Adding dependency context enables the validation of expected communication paths and improves the detection of routing issues that cannot be detected from the Kubernetes manifest alone. This makes dependency-aware static analysis a valuable addition to Kubernetes manifest integritiy.
In the aftermath of severe flooding, earthquakes, or warfare, the failure of ground-based cellular infrastructure often leaves affected populations and first responders without vital communications connectivity. While Unmanned Aerial Vehicles (UAVs) and 5G NR Device-to-Device (D2D) relaying offer a promising solution for rapid network restoration, a significant gap exists between terrestrial relaying standards and a functional, deployable aerial network. This thesis bridges this gap by designing a 3GPP-aligned operational model for UAV-based relaying.
By adapting mature 5G terrestrial relaying standards to the unique constraints of an aerial network, the proposed operational model introduces specific architecture enhancements to directly address the critical needs of these network-denied environments. For localised use cases through UE-to-UE (U2U) architectures, the operational model helps stranded individuals contact emergency services and supports local coordination. Additionally, for core-connected use cases through UE-to-Network (U2N) architectures, the operational model restores public network access, bridging the isolated disaster zone back to the core network.
The proposed operational model prescribes specific protocol procedures for synchronisation, discovery and connection establishment tailored for aerial networks. To overcome the high collision rates caused by standard decentralised Mode 2 protocols in high-density environments, the operational model introduces a critical architectural enhancement: UAV-governed scheduling, which leverages centralised coordination to ensure reliable out-of-coverage connectivity. Furthermore, the operational model incorporates the specific signalling exchanges required to retrieve real-time radio environment data, enabling the UAV to dynamically optimise its 3D position to maximise network throughput.
The effectiveness of the proposed operational model is systematically evaluated through system-level simulations. The simulations reveal that while standard Mode 2 resource allocation suffers from severe half-duplex and physical collisions under high network loads, the proposed UAV-governed scheduling eliminates steady-state collisions entirely. This coordination guarantees near-perfect packet delivery ratios and cuts end-to-end latency by more than half compared to the uncoordinated baseline. Ultimately, the results demonstrate that UAV-based relaying provides a highly effective 3GPP-aligned solution for rapid network restoration in an emergency scenario.
https://doi.org/10.4121/625a1cd3-63b8-40eb-895d-a20bdbdc0416 Repository link ...
By adapting mature 5G terrestrial relaying standards to the unique constraints of an aerial network, the proposed operational model introduces specific architecture enhancements to directly address the critical needs of these network-denied environments. For localised use cases through UE-to-UE (U2U) architectures, the operational model helps stranded individuals contact emergency services and supports local coordination. Additionally, for core-connected use cases through UE-to-Network (U2N) architectures, the operational model restores public network access, bridging the isolated disaster zone back to the core network.
The proposed operational model prescribes specific protocol procedures for synchronisation, discovery and connection establishment tailored for aerial networks. To overcome the high collision rates caused by standard decentralised Mode 2 protocols in high-density environments, the operational model introduces a critical architectural enhancement: UAV-governed scheduling, which leverages centralised coordination to ensure reliable out-of-coverage connectivity. Furthermore, the operational model incorporates the specific signalling exchanges required to retrieve real-time radio environment data, enabling the UAV to dynamically optimise its 3D position to maximise network throughput.
The effectiveness of the proposed operational model is systematically evaluated through system-level simulations. The simulations reveal that while standard Mode 2 resource allocation suffers from severe half-duplex and physical collisions under high network loads, the proposed UAV-governed scheduling eliminates steady-state collisions entirely. This coordination guarantees near-perfect packet delivery ratios and cuts end-to-end latency by more than half compared to the uncoordinated baseline. Ultimately, the results demonstrate that UAV-based relaying provides a highly effective 3GPP-aligned solution for rapid network restoration in an emergency scenario.
https://doi.org/10.4121/625a1cd3-63b8-40eb-895d-a20bdbdc0416 Repository link ...
In the aftermath of severe flooding, earthquakes, or warfare, the failure of ground-based cellular infrastructure often leaves affected populations and first responders without vital communications connectivity. While Unmanned Aerial Vehicles (UAVs) and 5G NR Device-to-Device (D2D) relaying offer a promising solution for rapid network restoration, a significant gap exists between terrestrial relaying standards and a functional, deployable aerial network. This thesis bridges this gap by designing a 3GPP-aligned operational model for UAV-based relaying.
By adapting mature 5G terrestrial relaying standards to the unique constraints of an aerial network, the proposed operational model introduces specific architecture enhancements to directly address the critical needs of these network-denied environments. For localised use cases through UE-to-UE (U2U) architectures, the operational model helps stranded individuals contact emergency services and supports local coordination. Additionally, for core-connected use cases through UE-to-Network (U2N) architectures, the operational model restores public network access, bridging the isolated disaster zone back to the core network.
The proposed operational model prescribes specific protocol procedures for synchronisation, discovery and connection establishment tailored for aerial networks. To overcome the high collision rates caused by standard decentralised Mode 2 protocols in high-density environments, the operational model introduces a critical architectural enhancement: UAV-governed scheduling, which leverages centralised coordination to ensure reliable out-of-coverage connectivity. Furthermore, the operational model incorporates the specific signalling exchanges required to retrieve real-time radio environment data, enabling the UAV to dynamically optimise its 3D position to maximise network throughput.
The effectiveness of the proposed operational model is systematically evaluated through system-level simulations. The simulations reveal that while standard Mode 2 resource allocation suffers from severe half-duplex and physical collisions under high network loads, the proposed UAV-governed scheduling eliminates steady-state collisions entirely. This coordination guarantees near-perfect packet delivery ratios and cuts end-to-end latency by more than half compared to the uncoordinated baseline. Ultimately, the results demonstrate that UAV-based relaying provides a highly effective 3GPP-aligned solution for rapid network restoration in an emergency scenario.
https://doi.org/10.4121/625a1cd3-63b8-40eb-895d-a20bdbdc0416 Repository link
By adapting mature 5G terrestrial relaying standards to the unique constraints of an aerial network, the proposed operational model introduces specific architecture enhancements to directly address the critical needs of these network-denied environments. For localised use cases through UE-to-UE (U2U) architectures, the operational model helps stranded individuals contact emergency services and supports local coordination. Additionally, for core-connected use cases through UE-to-Network (U2N) architectures, the operational model restores public network access, bridging the isolated disaster zone back to the core network.
The proposed operational model prescribes specific protocol procedures for synchronisation, discovery and connection establishment tailored for aerial networks. To overcome the high collision rates caused by standard decentralised Mode 2 protocols in high-density environments, the operational model introduces a critical architectural enhancement: UAV-governed scheduling, which leverages centralised coordination to ensure reliable out-of-coverage connectivity. Furthermore, the operational model incorporates the specific signalling exchanges required to retrieve real-time radio environment data, enabling the UAV to dynamically optimise its 3D position to maximise network throughput.
The effectiveness of the proposed operational model is systematically evaluated through system-level simulations. The simulations reveal that while standard Mode 2 resource allocation suffers from severe half-duplex and physical collisions under high network loads, the proposed UAV-governed scheduling eliminates steady-state collisions entirely. This coordination guarantees near-perfect packet delivery ratios and cuts end-to-end latency by more than half compared to the uncoordinated baseline. Ultimately, the results demonstrate that UAV-based relaying provides a highly effective 3GPP-aligned solution for rapid network restoration in an emergency scenario.
https://doi.org/10.4121/625a1cd3-63b8-40eb-895d-a20bdbdc0416 Repository link
Joint Communication and Sensing (JCAS) represents a key paradigm shift for future wireless systems, enabling efficient use of hardware and spectral resources. However, the integration of these two functions creates a fundamental challenge in resource management, as communication and sensing have conflicting performance objectives. This thesis addresses this challenge by developing a practical, simulation-based framework to optimize resource allocation in a communication-centric, mmWave JCAS system. A comprehensive system-level model is developed, which integrates time-frequency allocation, gain sharing, and beamforming at the Physical Resource Block - Transmission Time Interval level. Using this model, extensive Monte Carlo simulations are performed to characterize the trade-off between communication throughput and sensing accuracy, evaluated using practical metrics such as Root Mean Square Error (RMSE) and throughput. The results reveal that allocating joint resources in the center of the time-frequency grid outperforms the edge-based allocations using practical periodogram estimators. The proposed optimization tool successfully identifies the optimal resource parameters that maximize system performance under specific constraints. Furthermore, the analysis of an interference-nulling receiver provides critical insights into the trade-offs between interference suppression, noise enhancement, and beam robustness. Ultimately, this work provides a valuable design tool and a set of clear strategies for configuring the operational parameters of practical JCAS systems.
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Joint Communication and Sensing (JCAS) represents a key paradigm shift for future wireless systems, enabling efficient use of hardware and spectral resources. However, the integration of these two functions creates a fundamental challenge in resource management, as communication and sensing have conflicting performance objectives. This thesis addresses this challenge by developing a practical, simulation-based framework to optimize resource allocation in a communication-centric, mmWave JCAS system. A comprehensive system-level model is developed, which integrates time-frequency allocation, gain sharing, and beamforming at the Physical Resource Block - Transmission Time Interval level. Using this model, extensive Monte Carlo simulations are performed to characterize the trade-off between communication throughput and sensing accuracy, evaluated using practical metrics such as Root Mean Square Error (RMSE) and throughput. The results reveal that allocating joint resources in the center of the time-frequency grid outperforms the edge-based allocations using practical periodogram estimators. The proposed optimization tool successfully identifies the optimal resource parameters that maximize system performance under specific constraints. Furthermore, the analysis of an interference-nulling receiver provides critical insights into the trade-offs between interference suppression, noise enhancement, and beam robustness. Ultimately, this work provides a valuable design tool and a set of clear strategies for configuring the operational parameters of practical JCAS systems.
In smart office environments, intelligent control of IoT devices based on human movement and the monitoring of sedentary behavior are crucial. To address object occlusion problem, this thesis proposes an innovative solution: integrating UWB radars into ceiling-mounted office lights, in contrast to conventional wall-mounted setups. The core contribution of this work is the development of an indoor human localization and activity recognition system using ceiling-mounted radars. A novel method is proposed for joint human tracking and activity recognition. The system and the proposed method is validated in a 3.5 m × 6 m office environment with three ceiling-mounted Novelda X4 radars. The experimental results demonstrate high performance: trajectory tracking achieves a root mean square error (RMSE) as low as 0.23 m, representing a 52.6% improvement over the baseline method, while activity classification for walking, standing, and sitting reaches an accuracy of up to 98%. These findings demonstrate the feasibility and effectiveness of ceiling-mounted UWB radars for accurate human tracking and activity recognition in office-like environments.
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In smart office environments, intelligent control of IoT devices based on human movement and the monitoring of sedentary behavior are crucial. To address object occlusion problem, this thesis proposes an innovative solution: integrating UWB radars into ceiling-mounted office lights, in contrast to conventional wall-mounted setups. The core contribution of this work is the development of an indoor human localization and activity recognition system using ceiling-mounted radars. A novel method is proposed for joint human tracking and activity recognition. The system and the proposed method is validated in a 3.5 m × 6 m office environment with three ceiling-mounted Novelda X4 radars. The experimental results demonstrate high performance: trajectory tracking achieves a root mean square error (RMSE) as low as 0.23 m, representing a 52.6% improvement over the baseline method, while activity classification for walking, standing, and sitting reaches an accuracy of up to 98%. These findings demonstrate the feasibility and effectiveness of ceiling-mounted UWB radars for accurate human tracking and activity recognition in office-like environments.