Empowering Heterogeneous Communication Data Links in General Aviation through mmWave Signals

Wang, Wenbo; Talvitie, Jukka; Adamova, Eva Josth; Fath, Thilo; Korenčiak, Ľuboš; Valkama, Mikko; Lohan, Elena Simona

doi:10.1109/mwc.0001.1800593

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…At the same time, the air traffic growth also puts the secondary airports under pressure for greater capacity and increased safety. Therefore, low-cost surveillance solutions developed outside the ATM domain and relying on existing infrastructures, such as 5G networks have gained particular attention over the last few years, in particular in EU/SESAR-funded projects such as Emphasis [ 5 ], DroC2om [ 6 ], or NewSense [ 2 , 3 ]. Nevertheless, there are still very few studies focusing on 5G-based positioning and sensing for ATM applications, as most 5G-based solutions so far have been dedicated to improved wireless communications tasks [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, low-cost surveillance solutions developed outside the ATM domain and relying on existing infrastructures, such as 5G networks have gained particular attention over the last few years, in particular in EU/SESAR-funded projects such as Emphasis [ 5 ], DroC2om [ 6 ], or NewSense [ 2 , 3 ]. Nevertheless, there are still very few studies focusing on 5G-based positioning and sensing for ATM applications, as most 5G-based solutions so far have been dedicated to improved wireless communications tasks [ 5 , 6 ]. The research work on 5G applications for positioning, tracking, and/or LOS detection for ATM applications is still in the incipient phase, and there is still a literature gap with respect to suitable LOS detection solutions based on 5G signals; our paper aims to fill in this research gap.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

Jayawardana

Obaid

Yesilyurt

et al. 2023

Sensors

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The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

Jayawardana

Obaid

Yesilyurt

et al. 2023

Sensors

Self Cite

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“…Lacking the monitoring of the spectrum utilization leading to more spectrum conflicts toward existing users, the supervision of UAV communication behavior is major challenge in the federal aviation administration (FAA) [ 2 ]. Meanwhile, the multi-link operational concept (MLOC) [ 3 ] applied for the heterogeneous aeronautical network, such as the dual links with SatComm and cellular link [ 4 ], and mmWave with cmWave [ 5 ], is promising to increase the communication resilience. Moreover, MLOC ensures the reliability of UAV operations at the cost of the occupying multiple heterogeneous link resources dynamically.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Performance Analysis of Opportunistic Link Selection for UAV Communication

Petrunin

Tsourdos

2021

Sensors

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In anticipation of wide implementation of 5G technologies, the scarcity of spectrum resources for the unmanned aerial vehicles (UAVs) communication remains one of the major challenges in arranging safe drone operations. Dynamic spectrum management among multiple UAVs as a tool that is able to address this issue, requires integrated solutions with considerations of heterogeneous link types and support of the multi-UAV operations. This paper proposes a synthesized resource allocation and opportunistic link selection (RA-OLS) scheme for the air-to-ground (A2G) UAV communication with dynamic link selections. The link opportunities using link hopping sequences (LHSs) are allocated in the GCSs for alleviating the internal collisions within the UAV network, offloading the on-board computations in the spectrum processing function, and avoiding the contention in the air. In this context, exclusive technical solutions are proposed to form the prototype system. A sub-optimal allocation method based on the greedy algorithm is presented for addressing the resource allocation problem. A mathematical model of the RA-OLS throughput with above propositions is formulated for the spectrum dense and scarce environments. An interference factor is introduced to measure the protection effects on the primary users. The proposed throughput model approximates the simulated communication under requirements of small errors in the spectrum dense environment and the spectrum scarce environment, where the sensitivity analysis is implemented. The proposed RA-OLS outperforms the static communication scheme in terms of the utilization rate by over 50% in case when multiple links are available. It also enables the collaborative communication when the spectral resources are in scarcity. The impacts from diverse parameters on the RA-OLS communication performance are analyzed.

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NEMO Routing Optimization for Aviation Communication Based on Simulated Annealing Algorithm

Huang,

Zhang,

Zhang

2023

Proceedings of the 8th International Conference on Cyber Security and Information Engineering

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Empowering Heterogeneous Communication Data Links in General Aviation through mmWave Signals

Cited by 3 publications

References 12 publications

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

Modeling and Performance Analysis of Opportunistic Link Selection for UAV Communication

NEMO Routing Optimization for Aviation Communication Based on Simulated Annealing Algorithm

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