Performance analysis and resourceallocation in underlaid device-to-device cellular networks
Notice bibliographique
Résumé
Device-to-device (D2D) communications enables direct connection between nearby cellular users without traversing through the base-station (BS).Potential benefits of D2D communications in cellular networks are multi-folds, ranging from enhancing spectrum efficiency, reducing network congestion, shortening packet delay, saving power, to enabling location-based applications and services.D2D-enabled networking not only has been required for public safety networks when the cellular coverage is not available, but also developed as supporting technologies for Internet of Things (IoT) and Vehicle-to-Everything (V2X) connections.As D2D is allowed to operate in the same spectrum with cellular users, the more resources (e.g., time-frequency) are shared between D2D and cellular transmission, the more interference will be present, yet higher potential spectral efficiency gains will be provided if the interference can be effectively managed.This thesis considers underlaid D2D schemes where the D2D links fully reuse time-spectrum resources that currently occupied by the cellular transmission.Our focus is to investigate the benefits offered by underlaid D2D in terms of spectral efficiency gains via performance analysis and sum-rate maximizing resource allocation algorithms.First, this thesis develops a Gaussian-Mixture (GM) model to represent the aggregate interference at a typical D2D receiver in underlaid D2D cellular networks.From information theoretical point-of-view, the corresponding D2D link can be thought of as an additive quadrature GM channel.We then study the characterization of optimal input and the computation of capacity of such GM channel under an average power constraint.It is shown that the capacity-achieving input distribution has a uniformly distributed phase, while the optimal amplitude distribution includes a finite number of mass points.Our numerical examples illustrate that, in many cases, the capacityachieving distribution consists of only one or two mass points.Second, the analysis of achievable sum-rate offered by D2D communications is extended from link to network level.Both single-and multi-cell settings are considered in which multiple D2D links reuse the channel (time-frequency resources) currently occupied by one cellular uplink transmission.In addition, we assume full-duplex (FD) operation at D2D links to ease the channel assignment for underlaid D2D as FD D2D only requires one carrier frequency for both transmitting and receiving signals.Utilizing stochastic geometry based models to capture the randomness and mobility of D2D/cellular users, analytical sum-rate expressions are derived and applied to investigate the effects of network parameters on the achieved sum-rates.It is demonstrated that, from an average throughput perspective, FD D2D brings performance improvements as compared to the half-duplex (HD) counterpart and pure cellular systems (in absence of D2D).Third, the single-antenna multi-cell network model is expanded to include multi-antenna trans-Ngoc, for his support, encouragement, and advice during my graduate study at McGill University.Without his assistance and dedicated involvement in every steps throughout the process, this thesis would have never been accomplished.I also learned very much from his vast knowledge, genuine enthusiasm toward research, hard work, and his sense of humour.I am grateful to Dr. Nghi Tran, whose suggestions and enormous knowledge in wireless communications and mathematics have benefited me tremendously.I would also like to thank Prof. Benoit Champagne, Prof. Jun Cai
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 | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».