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Publisher DOI: 10.14279/tuj.eceasst.80.1164
Title: Time- and frequency-domain dynamic spectrum access: learning cyclic medium access patterns in partially observable environments
Language: English
Authors: Lindner, Sebastian  
Stolpmann, Daniel  
Timm-Giel, Andreas  
Keywords: Artificial Neural Networks; Cognitive Radio; Communication System Coexistence; Dynamic Spectrum Access; Machine Learning
Issue Date: 2021
Publisher: Techn. Univ. Berlin
Source: Electronic Communications of the EASST 80: 1-14 (2021)
Abstract (english): 
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
Conference: Conference on Networked Systems 2021, NetSys 2021 
DOI: 10.15480/882.4043
ISSN: 1863-2122
Journal: Electronic communications of the EASST 
Institute: Kommunikationsnetze E-4 
Document Type: Article
Project: Machine Learning in Aeronautical Communications 
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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