Special Issue on 5G Communications and Experimental Trials with Heterogeneous and Agile Mobile networks
Notice bibliographique
Résumé
5th generation mobile networks, 5G, are the proposed next-generation communication network standards.In addition to providing more than 1 Gb per second faster speeds, 5G will be a global game changer from technological, economic, societal, and environmental perspectives by integrating multiple networks in diverse sectors for various up to date applications such as the Internet of Things (IoT), device-to-device direct communication (D2D), vehicular communications (V2X), and disaster resilient communication.Currently, millimeter-wave (mmWave) radio spectrum between 30 GHz and 300 GHz is critical for 5G rollout.Regulatory bodies around the world are now working towards opening up new spectrum bands from 6 GHz-100 GHz, and new technologies to overcome the challenges of these mmWave bands have been developed.It is envisioned that advanced 5G network infrastructure includes ultra-broadband access, high-speed backhaul and relay, softwarized flexible evolved packet core solutions for efficient system management, and satellite communications as an inherent component of 5G systems.In this Special Issue, we attempted to select papers covering both experimental trials as well as system-oriented issues over heterogeneous and agile mobile network environments.The invited paper "5GCHAMPION -Disruptive 5G Technologies for Roll-Out in 2018" by Emilio Calvanese Strinati et al. summarizes the 5GCHAMPION Europe-Korea collaborative project that provides fully integrated and operational disruptive 5G technologies over a global scope.This article focuses on a subset of three disruptive solutions including high-speed communications, direct satellite-UE (user equipment) communications, and softwareization over virtualized infrastructure.5GCHAMPION technologies are developed and deployed for the 2018 PyeongChang Olympic Games in Korea.Extensive real-field experimentation for evaluating the effectiveness and performability of these 5G principal features enhances real maturity of 5G technologies and applications for large-scale 5G services.The next paper titled "Field Measurement-Based Received Power Analysis for Directional Beamforming Millimeter-Wave Systems: Effects of Beamwidth and Beam Mis-Alignment" by Juyul Lee et al. overcomes many propagation limitations of the millimeter wave (mmWave) frequency band by using an enhanced beamforming technology.Notably, To overcome the performance variation and extra power losses of beam-based mmWave communication system caused by the width and operational methods of beamforming, they investigated and designed directional beamforming approaches in consideration of the aspect of beam width and alignment effect.The third paper "Stochastic Channel Modeling for Railway Tunnel Scenarios at 25 GHz" by Danping He et al. tackles high-speed 5G communication scenarios for the railway system.In support of the high-speed trains, the proposed communication system is designed to handle high data rate demands with seamless connectivity over high mobility.The authors examined and modeled channel characteristics for railway tunnel scenario with both straight and curved routes.They calibrated and validated the target scenarios using a 3D ray tracing (RT) rendering technique with the "Mobile Hotspot Network (MHN)" system based measurements.Their additional RT simulation results at 25.25 GHz with 500 MHz bandwidth validate the channel characteristics models.According to the substantial experiments, they consolidated several channel parameters to a 3GPP-like stochastic channel generator to get the practical channel information, which can reproduce similar scenarios for both link and system level designs of the communication system.The analysis of Open Loop (OL)/Transmit Power Control (TPC) parameters is crucial for efficient resource management of cellular networks.The following paper "Investigation of Open Loop Transmit Power Control
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,050 | 0,018 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».