Recent Advances in Cellular D2D Communications
Notice bibliographique
Résumé
Device-to-device (D2D) communications have attracted a great deal of attention from researchers in recent years.It is a promising technique for offloading local traffic from cellular base stations by allowing local devices, in physical proximity, to communicate directly with each other.Furthermore, through relaying, D2D is also a promising approach to enhancing service coverage at cell edges or in black spots.Besides improving network performance and service quality, D2D can open up opportunities for new proximity-based services and applications for cellular users.However, there are many challenges to realizing the full benefits of D2D.For one, minimizing the interference between legacy cellular and D2D users operating in underlay mode is still an active research issue.With the 5th generation (5G) communication systems expected to be the main data carrier for the Internet-of-Things (IoT) paradigm, the potential role of D2D and its scalability to support massive IoT devices and their machine-centric (as opposed to human-centric) communications need to be investigated.New challenges have also arisen from new enabling technologies for D2D communications, such as non-orthogonal multiple access (NOMA) and blockchain technologies, which call for new solutions to be proposed.This special issue aims to present a collection of exciting papers, reporting the most recent advances in cellular D2D communications.Through invited and open call submissions, a total of ten excellent articles have been accepted, following a rigorous review process that required a minimum of three reviews and at least one revision round for each paper.The list of accepted articles includes one review and nine original research articles on addressing many of the aforementioned challenges and beyond.The first paper by Höyhtyä, Apilo and Lasanen [1] is a review article that analyzed the latest energy consumption models of 3GPP standardized LTE (long-term evolution) and WiFi interfaces, with recommendations on energy saving options for D2D communications in a set of application scenarios.Distributed resource sharing and allocation are amongst the most important issues in cellular D2D networks.Hong, Wang, Cai and Leung [2] investigated the issue of fairness in cooperative D2D computational resource sharing, and proposed a blockchain-based credit system where user's computational task cooperation are recorded on public blockchain ledger as transactions, and their credit balance can be easily accessed from the ledger.The performance of the proposed credit system is demonstrated by incorporating it into a connectivity-aware task scheduling scheme to enforce fairness among users in the D2D network.Radio resource is another resource type that must be efficiently managed.The next four papers explore different strategies for allocating radio resources such spectrum and transmit power for D2D communications.Jiang, Wang, Ren and Xu [3] studied the problem of spectrum resource and transmit power allocation for underlay multicast D2D communications, and presented a heuristic and low-complexity resource and power allocation scheme that aims to maximize overall energy efficiency, while satisfying the QoS (quality of service) requirements of both cellular and D2D users.Similarly, for underlay D2D communications, Ban [4] proposed a practical scheme with low Future Internet 2018, 10, 10 1 www.mdpi.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,020 |
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 ».