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Time- and frequency-domain dynamic spectrum access: learning cyclic medium access patterns in partially observable environments
Citation Link: https://doi.org/10.15480/882.4043
Publikationstyp
Journal Article
Date Issued
2021
Sprache
English
Author(s)
Institut
TORE-DOI
Volume
80
Start Page
1
End Page
14
Citation
Electronic Communications of the EASST 80: 1-14 (2021)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Techn. Univ. Berlin
Upcoming communication systems increasingly often tackle the spectrum scarcity problem through the coexistence with legacy systems in the same frequency band. Cognitive Radio presents popular methods for Dynamic Spectrum Access (DSA) that enable coexistence. Historically, DSA meant a separation solely in the frequency domain, while in recent years it has been extended through the dimension of time, by employing Machine Learning to learn semi-deterministic and cyclic medium access patterns of the legacy system that are observed through channel sensing. When this pattern is learnable, then a new system can utilize a neural network and predict future medium accesses, thus steering its own medium access. We investigate this novel and more fine-grained version of DSA, propose a predictor and show its capability of reliably predicting future medium accesses of a legacy system in an aeronautical coexistence scenario. We extend the predictor to the case of partial observability, where only a narrowband receiver is available, s.t. observations are limited to a single sensed channel per time slot. In particular, we propose a custom loss function that is tailored to partially observable environments. In the spirit of Open Science, all implementation files are released under an open license
Subjects
Artificial Neural Networks
Cognitive Radio
Communication System Coexistence
Dynamic Spectrum Access
Machine Learning
MLE@TUHH
DDC Class
004: Informatik
Publication version
publishedVersion
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