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Item type:Publication, Access status: without files , Predictive medium access for wireless networks applied to aeronautical communicationsPublicationtype: Doctoral Thesis39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Multi-channel self-organized TDMA for future aeronautical mobile ad-hoc networks(IEEE, 2024-03-21); ; ; Modernizing communication systems is crucial for the future of aviation, and the <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula>-dacs Digital Aeronautical Communications Systemin(LDACS) in Air-Ground (A/G) and Air-Air (A/A) modes is being developed European and American organizations to meet this need. In this study, we explore possible channel access schemes for aeronautical ad-hoc networks. We propose Multi-Channel Self-Organized TDMA (MCSOTDMA), a novel variant of randomized, reservation-based slottedALOHA for LDACS A/A. In particular, we prove that it can achieve an optimal delay by following the <inline-formula><tex-math notation="LaTeX">$\frac{1}{e}$</tex-math></inline-formula>-rule of traditional slotted ALOHA's throughput. This new variant allows users to distributively converge towards an optimal channel access strategy with no a-priori knowledge of their neighborhood. It scales to any number of users and always guarantees a freely chosen, average packet collision probability. We evaluate the performance on the busiest oceanic airspace, and compare it to medium access schemes used in maritime and other aeronautical networks. We demonstrate that arbitrary collision rates can be achieved at the cost of proportional packet delay, establish communication links with deterministic performance, and implement broadcast voice messaging among pilots. We provide access to the simulator and a protocol specification under open licenses, in line with the principles of open science.Publicationtype: Journal Article43 - Some of the metrics are blocked by yourconsent settings
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 , A Tutorial on Trace-based Simulations of Mobile Ad-hoc Networks on the Example of Aeronautical CommunicationsThe OMNeT++ simulator is well-suited for the simulation of randomized user behavior in communication networks. However, there are scenarios, where such a random model is unsuited to evaluate a communication system, and this paper attempts to highlight such a case. Using this example of ad-hoc communication between aircraft mid-flight, a tutorial-style description is attempted that shall show how the OMNeT++ simulator can be used when a wealth of real-world trace data is available. In particular, it is described how mobility trace files can be directly used within OMNeT++, and how to link the generation of data messages to this mobility data. This is explained via an example simulation that evaluates a communication network in which an aircraft notifies the ground control when it enters or leaves a specific geographic region. Additionally, a novel trace-based application has been developed to achieve this link between mobility and message generation. Furthermore, a new TDMA-based medium access protocol for decentralized communication networks is presented, which is oracle-based and thus allows a TDMA-like behavior of medium access without causing any overhead; it can be useful when upper-layer protocols should be evaluated under the assumption of TDMA-like behavior, but isolated from the effects of a full-fledged TDMA protocol. Finally, physical layer behavior is often either overly simplistic or overly computationally expensive. For the latter case, when a detailed channel model is available but its evaluation requires prohibitive computational effort, then averaging its behavior into trace data can find a middle ground between efficient evaluation and realistic representation. Hence, a novel trace-based radio model has been developed that makes use of an SNR to PER mapping. In the spirit of open science, all implementations have been made available under open licenses.Publicationtype: Conference Paper30 31 - 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 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Analytic study of packet delay from 4G and 5G system ARQs using Signal Flow Graphs(IEEE, 2020-05); ; ; ; Modern mobile networks in the era of 5G have ambitious performance goals. One envisioned goal is to have communication that is ultra-reliable and ultra-low latency (uRLLC). To ensure reliability, Automatic Repeat Request (ARQ) processes are usually deployed, sometimes combined with forward error correction. As an evolution of the Long Term Evolution (LTE) system, 5G may be foreseen to use similar methods, and so we assume LTE's Hybrid ARQ on the Medium Access Control (MAC) layer, and a Selective Repeat ARQ on the Radio Link Control (RLC) layer. Simulation of LTE systems is a time-consuming endeavor due to its complexity, and when latency and reliability are the key performance indicators, it may be preferable to isolate the ARQ processes. In this manner, this paper presents a mathematical model of the two stacked ARQ processes that are used in today's LTE system. It is based on the method of Signal Flow Graphs (SFGs) and allows the analysis of either ARQ process in isolation, and of both stacked on top of each other. Analytic results are shown for a realistic set of parameters, and in the spirit of Open Science, the MATLAB implementation files are made available in the conclusion to interested researchers, so that verification and an adaption to similar ARQ processes is easily made.Publicationtype: Conference PaperScopus© Citations 3 382 677 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A two-game algorithm for device-to-device resource allocation with frequency reuseNext generation mobile systems aim to meet the strict demands of an exponentially increasing number of users. Particularly device-to-device (D2D) communication has attracted a lot of attention during the last years. D2D communication can ofïoad cellular trafïc off the base station. It may reuse radio frequency resources simultaneously used by cellular communication for D2D transmissions, thereby increasing spectral efïciency and system capacity. However, reusing frequency resources can lead to in-cell interference. The expected interference must therefore be actively mitigated as part of the scheduling process. We investigate a novel joint radio resource scheduling and allocation algorithm for D2D communication that makes use of two methods from the ïeld of game theory. It aims to meet the respective userâ™s Quality of Service requirements and strives for fairness between users by playing a Transferable Utility game. At the same time it actively mitigates in-cell interference from frequency resource reuse and determines an optimal transmission power through Stackelberg gameplays. It is shown through simulation that system performance increases if resource allocation and reuse is done in a coordinated manner. The proposed scheduler scales well due to its polynomial computational complexity and outperforms baseline schedulers and the single-game approach in several simulated scenarios.Publicationtype: Conference PaperScopus© Citations 3 284 331