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Item type:Publication, Access status: without files , Hidden node-aware dynamic spectrum access using deep learning for coexisting aeronautical communication systems(IEEE, 2023-10); ; ; ; We propose a novel approach based on deep learning to address the hidden node problem which occurs in the coexistence of aeronautical communication standards. The modern aeronautical communication standard L-band Digital Aeronautical Communications System (LDACS) in Air-Air (A/A) mode needs to share spectrum with the Distance Measuring Equipment (DME), which is a legacy system. As DME is safety-critical, causing interference on it must be avoided for all newly proposed aeronautical systems spectrally coexisting with it. Recently, cognitive radio techniques have been proposed for LDACS A/A to access spectrum dynamically and to overcome the limitations of static approaches. For this, a Recurrent Neural Network (RNN) was trained to predict idle time slots on those frequency bands, where both systems operate. By exploiting patterns in the spectrum access of DME, a promising amount of idle resources could be predicted. However, previous approaches would perform poorly in a real-world deployment, as they did not take the hidden node problem into account.This paper formulates the hidden node problem for the case that an LDACS A/A user is within communication range of a DME ground station, but not within range of all airborne DME users connected to it. Through statistical analysis, we underline the problem's significance in practical cases. We simulate the coexistence of the two systems from a channel access perspective, taking signal propagation and the behavior of the ground station into account. Further, we present an RNN that is able to predict the channel access of hidden nodes. The key idea of our algorithm is to exploit the fact that while DME request pulses from airborne users may appear as hidden, response pulses from the ground station will be visible. Our results show that by inferring DME request channel activity from the response channel, the hidden node problem can be overcome effectively. By using our approach, nearly the same performance can be achieved as in the idealized case where all nodes are visible.Publicationtype: Conference Paper29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Deep Learning-Based Dynamic Spectrum Access for Coexistence of Aeronautical Communication SystemsPublicationtype: Conference Paper124 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Time- and frequency-domain dynamic spectrum access: learning cyclic medium access patterns in partially observable environmentsUpcoming 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 licensePublicationtype: Journal ArticleScopus© Citations 1 123 238 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Predictive scheduling and opportunistic medium access for shared-spectrum radio systems in aeronautical communication(Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V. (DGLR), 2020-10-09); ; Cognitive Radios (CRs) tackle the spectrum scarcity problem by allowing unlicensed access on already-licensed spectrum. Where primary user (PU) radio systems hold a privileged medium access, secondary users (SUs) try to utilize and use spare time-frequency resources to establish communication. At their core, CRs need to sense, detect and predict the medium access pattern of PUs in order to facilitate communication. A promising approach for inferring these predictions is the use of Machine Learning techniques and in particular Artifical Neural Networks (ANNs). ANNs try to learn a mapping from a set of inputs to a specific desired output and can therefore be directly applied to the problem of PU activity prediction. Especially Recurrent Neural Networks (RNNs) show adequate performance for noisy measurement data and prolonged training periods. In this work, the applicability of ANN-based channel state prediction is examined via a case study on the upcoming aeronautical communication technology L-band Digital Aeronautical Communications System (LDACS). LDACS is envisioned to reuse spectral resources currently primarily allocated to other aeronautical systems such as the Distance Measuring Equipment (DME) system. Primary and secondary users are modeled with respect to preliminary LDACS specifications and the performance of the proposed algorithms is evaluated via simulation. We show that RNNs can function as prediction agents for SU LDACS medium access and that stringent reliability and interference requirements can be met. A supervised learning problem, together with an incremental learning strategy is proposed to address time-varying PU channel access patterns. Finally, a brief discussion on the use of predictions in a distributed scheduling approach is given.Publicationtype: Conference Paper437 226 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Coexistence of Shared-Spectrum Radio Systems through Medium Access Pattern Learning using Artificial Neural Networks(2020-09); ; Spectrum scarcity requires novel approaches for sharing frequency resources between different radio systems. Where coordination is not possible, intelligent approaches are needed, allowing a novel 'secondary' system to access unused resources of a legacy (primary) system without requiring modifications of this primary system. Machine Learning is a promising approach to recognize patterns of the primary system and adapt the channel access accordingly. In this contribution we investigate the capability of Feed-Forward Deep Learning and Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) to detect communication patterns of the primary user. Therefore we take the example of a new aeronautical system (LDACS) coexisting with three different systems. Firstly the coexistence with the Distance Measurement Equipment (DME) providing a deterministic interference to the secondary user and secondly with two synthetic channel access patterns, realized by a 2-state Markov model, modeling a bursty channel access behavior, as well as through a sequential channel access model.It can be shown that the Markov property of a Gilbert-Elliot channel model limits the predictability; nonetheless, we show that the model characteristics can be fully learned, which could leverage the design of interference avoidance systems that make use of this knowledge. The determinism of DME allows an error-free prediction, and it is shown that the reliability of sequential access model prediction depends on the model's parameter. The limits of Feed-Forward Deep Neural Networks are highlighted, and why LSTM RNNs are state-of-the-art models in this problem domain. We show that these models are capable of online learning, as well as of learning correlations over long periods of time.In the spirit of open science, the implementation files are made available in the conclusion.Publicationtype: Conference Paper63