1,720,967 research outputs found
Technology recognition and traffic characterization for wireless technologies in ITS band
Get PDFThe research leading to this article has received funding from the European H2020 Program under grant agreements 101016499 (DEDICAT6G project) . Besides, part of the results presented in this work were obtained through experimentation on the Belgian Smart Highway Testbed which is partially supported by the Flemish Min-istry of Mobility and Public Works (MOW) and the regional Agency for Roads and Traffic (AWV) , Belgium
Machine learning enabled Wi-Fi saturation sensing for fair coexistence in unlicensed spectrum
Get PDFIn the past few years, machine learning (ML) techniques have been extensively applied to provide efficient solutions to complex wireless network problems. As such, Convolutional Neural Network (CNN) and Q-learning based ML techniques are most popular to achieve harmonized coexistence of Wi-Fi with other co-located technologies such as LTE. In the existing coexistence schemes, a co-located technology selects its transmission time based on the level of Wi-Fi traffic generated in its collision domain which is determined by either sniffing the Wi-Fi packets or using a central coordinator that can communicate with the co-located networks to exchange their status and requirements through a collaboration protocol. However, such approaches for sensing traffic status increase cost, complexity, traffic overhead, and reaction time of the coexistence schemes. As a solution to this problem, this work applies a ML-based approach that is capable to determine the saturation status of a Wi-Fi network based on real-time and over-the-air collection of medium occupation statistics about the Wi-Fi frames without the need for decoding. In particular, inter-frame spacing statistics of Wi-Fi frames are used to develop a CNN model that can determine Wi-Fi network saturation. The results demonstrate that the proposed ML-based approach can accurately classify whether a Wi-Fi network is saturated or not
Residual service time optimization for legacy wireless-TSN end nodes
Get PDFThe emergence of Time-Sensitive Networking (TSN) has enabled network determinism to a new level, offering high reliability and bounded latency for critical communications. However, the unpredictable nature of traffic generation also poses new challenges to TSN. While TSN is designed to maintain backward compatibility with the 802.1 standards, many end nodes may not be equipped to understand TSN. This can result in a less deterministic TSN, and suboptimal resource utilization, mainly driven by Residual Service Time (RST). To address these challenges, this study proposes three scheduling mechanisms to reduce RST: q-learning, active time slot update, and polynomial forecasting. Real-world data captured from our wireless-TSN (W-TSN) evaluation kit is used to compare the proposed approaches in terms of one-way latency. The results show that the machine learning approach outperforms the other methods in terms of overall latency. However, it is less effective in identifying the optimal time slot position compared to the other methods
Coexistence scheme for uncoordinated LTE and WiFi networks using experience replay based Q-learning
Get PDFNowadays, broadband applications that use the licensed spectrum of the cellular network are growing fast. For this reason, Long-Term Evolution-Unlicensed (LTE-U) technology is expected to offload its traffic to the unlicensed spectrum. However, LTE-U transmissions have to coexist with the existing WiFi networks. Most existing coexistence schemes consider coordinated LTE-U and WiFi networks where there is a central coordinator that communicates traffic demand of the co-located networks. However, such a method of WiFi traffic estimation raises the complexity, traffic overhead, and reaction time of the coexistence schemes. In this article, we propose Experience Replay (ER) and Reward selective Experience Replay (RER) based Q-learning techniques as a solution for the coexistence of uncoordinated LTE-U and WiFi networks. In the proposed schemes, the LTE-U deploys a WiFi saturation sensing model to estimate the traffic demand of co-located WiFi networks. We also made a performance comparison between the proposed schemes and other rule-based and Q-learning based coexistence schemes implemented in non-coordinated LTE-U and WiFi networks. The simulation results show that the RER Q-learning scheme converges faster than the ER Q-learning scheme. The RER Q-learning scheme also gives 19.1% and 5.2% enhancement in aggregated throughput and 16.4% and 10.9% enhancement in fairness performance as compared to the rule-based and Q-learning coexistence schemes, respectively
Intelligent Spectrum Sharing Between LTE and Wi-Fi Networks using Muted MBSFN Subframes
Get PDFDue to the fast growth of diverse wireless network deployments, the radio spectrum is becoming scarce. Hence, it is beneficial that different radio access technologies share the spectrum in a harmonious way. In this paper, we propose a co-existence scheme between Long Term Evolution (LTE) and Wi-Fi networks that utilizes a Multimedia Broadcast Multicast Service (MBMS) over a Single Frequency Network (MBSFN) feature of an LTE network. MBSFN is an LTE feature that provides support for multicast/broadcast traffic. We propose an adaptive scheme that configures muted subframes, initially intended for MBSFN operation, to allow Wi-Fi transmissions. For the adaptive configuration of muted MBSFN subframes, the LTE eNB uses its traffic queue and the Wi-Fi spectrum occupancy information, which is determined by a convolutional neural network-based technology recognition and traffic characterization system. The standard LTE System Information Blocks are used to convey the updated configuration to the LTE UE. Hence, the proposed coexistence scheme doesn’t require any modifications to a standard MBSFN-compliant LTE UE. Performance analysis is done for various traffic situations, and the results show that muted MBSFN subframe-based coexistence gives a 15% improvement in average aggregated throughput as compared to using Almost Blank Subframe-based coexistence
Intelligent spectrum sharing mechanisms for heterogeneous wireless access networks
No full textDe laatste tijd neemt het gebruik van draadloze communicatieapparaten die zijn verbonden met verschillende netwerken, zoals Wi-Fi, 4G en 5G, snel toe. Naarmate deze groei voortduurt, wordt het van cruciaal belang om het beperkte spectrum waarbinnen deze apparaten moeten werken efficiënt te beheren. Stel je de communicatie tussen deze apparaten voor als een volgepakte kamer met mensen die tegen elkaar praten; soms kunnen hun gesprekken elkaar overlappen, waardoor verwarring ontstaat. Een soortgelijk scenario, interferentie genoemd, doet zich voor wanneer verschillende, op dezelfde locatie geplaatste draadloze netwerkapparaten hun signaal tegelijkertijd in hetzelfde spectrum verzenden. Als oplossing voor dit probleem richt dit proefschrift zich op het ontwikkelen van effectieve mechanismen voor het delen van spectrum tussen verschillende draadloze technologieën in dezelfde spectrumband. De studie onderzoekt mechanismen om verschillende soorten draadloze signalen en dataverkeerspatronen te herkennen en stelt vervolgens methoden voor om spectrum efficiënter te delen. Het doel is ervoor te zorgen dat verschillende draadloze technologieën naast elkaar kunnen bestaan, zodat gebruikers een betere servicekwaliteit over een breder spectrum kunnen ervaren. Het onderzoek omvat verschillende methoden die machine learning-technieken gebruiken om signalen van verschillende apparaten die gebruik maken van allerhande draadloze netwerken te identificeren en op basis daarvan beslissingen over spectrumresourcebeheer te nemen
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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