UAV and IRS-aided wireless communications: modeling, analysis and optimization
Notice bibliographique
Résumé
With the roll-out of 5G in many countries e.g. South Korea, Canada, etc, the discussions on the development of 6G are overtaking the attention of both academia and industry. In comparison to 5G, 6G will bring a wider frequency band, higher coverage and spectral efficiency, higher data rate, low latency and more, to enhance communication for the massive number of users/devices. Two of the emerging enablers for beyond (5G) B5G and 6G communications are unmanned aerial vehicles (UAV) and intelligent reflecting surfaces (IRSs). When UAVs have already become an integral part of 5G and B5G to meet the high data rate requirement by offering better communication links due to the proactive placement at heights (i.e., flexible deployment in all the three dimensions (3D)), the IRSs have emerged as a key enabler for realizing 6G to provide better coverage by tuning the wireless environment through an intelligent reflection of the incoming signal at very little power cost. In addition, B5G networks are expected to support aerial user communications in accordance with the expanded requirements of data transmission for an aerial user. However, there are some challenges and unaddressed issues that need attention before accommodating the enablers like UAVs and IRSs in future wireless standards such as the consideration of different radio wave propagation properties between terrestrial areas and aerial areas, the effect of product path-loss experienced by IRS for both ground and aerial communication, gains associated with 3D IRS installation in static (on fixed location e.g. building height) and dynamic (on mobile UAV) environment are still unknown, and providing service to remote or un-served users on both small and large scale. In order to address the technical issues in the above context, the thesis develops few innovative enabling frameworks regarding UAV and/or IRS-assisted communication. In particular, (i) I begin with the optimal UAV-assisted data ferrying scheme to provide coverage to hard-to-reach or remote areas for delay-tolerant applications by taking aerial and ground channel model and rotary-wing UAV power consumption into account, (ii) Second, an integrated UAV-IRS scheme is proposed where IRS is installed on the UAV and therefore, can benefit from 3D deployment similar to UAV, taking important factors e.g. aerial and ground channel, height and number of IRS elements for UAV and IRS, respectively, and power consumption of both UAV and IRS in to account. Finally, (iii) to incorporate massive devices of different kinds in to consideration, I proposed large-scale IRS-assisted downlink communication for multi-BS and multi-IRS setup. For the very setup, the user association with indirect IRS-assisted communication and user association with direct BSs are studied. The impact of the fraction of each type of user, number of IRS elements and its power consumption, large-scale deployment intensity of UAV and IRS in to account. Additionally, the performance of the proposed schemes are investigated through the derivation of closed-form exact and/or accurate approximate expression of the performance metrics that include coverage/outage probability, spectral and energy efficiency, bit-error rate, etc. Finally, the provided expressions are utilized in the various optimization problem and optimization solutions are either convexified by reformulation and using approximations or by designing low complexity algorithms. The insights are drawn related to available operation modes and other critical network parameters. Finally, the results are validated through Monte-Carlo simulations.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».