Machine learning-based dynamic spectrum access for aircraft-to-aircraft communication under coexistence with legacy radio systems

Publikationstyp
Master Thesis
Publikationsdatum
2022-06-30
Sprache
English
Author
Algarra Ulierte, Teresa de Jesus orcid-logo
Kommunikationsnetze E-4 
Referee
Timm-Giel, Andreas orcid-logo
Turau, Volker 
Lindner, Sebastian
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2022-07-09
Institut
Kommunikationsnetze E-4 
DOI
10.15480/882.5107
TORE-URI
http://hdl.handle.net/11420/15292
Citation
Technische Universität Hamburg (2022)
Peer Reviewed
false
Spectrum scarcity is viewed as one of the key obstacles in the area of wireless communications. The lack of available unlicensed resources is impairing the development of newer and more modern communications systems. This is the case of L-band Digital Aeronautical Communications System (LDACS), an innovative air communication system that aims to use the part of the frequency spectrum licensed by Distance Measuring Equipment (DME), a legacy radio navigation system. DME has a low channel utilization rate, leaving idle numerous resources that could be used by LDACS through the use of Dynamic Spectrum Access (DSA) in Cognitive Radio (CR). In order to avoid interferences and collisions while taking advantage of these idle resources, this thesis proposes a new LDACS Machine Learning (ML)-based Medium Access Control (MAC). It incorporates a Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) in order to observe, learn, predict and avoid the DME licensed users. The results from this new MAC are analyzed and compared to two alternative approaches, showing the advantages of a ML-based approach.
Schlagworte
L-band Digital Aeronautical Communications System (LDACS)
Distance measuring Equipment (DME)
Machine learning (ML)
Cognitive Radio
Dynamic Spectrum Access
DDC Class
600: Technik
620: Ingenieurwissenschaften
Lizenz
https://creativecommons.org/licenses/by/4.0/
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