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Variables générées pour cette modification
| Variable | Valeur |
|---|---|
Si la modification est marquée comme mineure ou non (minor_edit) | |
Nom du compte d’utilisateur (user_name) | Ufa191vipi |
Groupes (y compris implicites) dont l'utilisateur est membre (user_groups) | *
user
autoconfirmed
|
Si un utilisateur est ou non en cours de modification via l’interface mobile (user_mobile) | |
Numéro de la page (article_articleid) | 0 |
Espace de noms de la page (article_namespace) | 0 |
Titre de la page (sans l'espace de noms) (article_text) | You Do Not Must Be A Big Corporation To Start Online Casino Free Play No Deposit |
Titre complet de la page (article_prefixedtext) | You Do Not Must Be A Big Corporation To Start Online Casino Free Play No Deposit |
Action (action) | edit |
Résumé/motif de la modification (summary) | |
Ancien modèle de contenu (old_content_model) | |
Nouveau modèle de contenu (new_content_model) | wikitext |
Ancien texte de la page, avant la modification (old_wikitext) | |
Nouveau texte de la page, après la modification (new_wikitext) | <br> Reinforcement Learning (RL) based mostly optimization of on-line caching by assuming a backhaul mode. Signal-to-Noise-Ratio (SNR) efficiency by considering also the optimization of uncoded caching methods. On this work, we study the adaptive number of backhaul and fronthaul transfer modes with the goal of optimizing the efficiency of content material delivery. The backhaul mode permits the switch of knowledge packets from the BBU within the cloud to the eRRHs. The backhaul mode permits the caches of the eRRHs to be updated, which may lower future supply latencies. In contrast, the fronthaul mode allows cooperative C-RAN transmissions which will reduce the present supply latency.<br><br><br> The backhaul mode transfers fractions of the requested files, whereas the fronthaul mode transmits quantized baseband [https://Ufa191Vip.com/ ufa191vip.com] samples as in Cloud-RAN (C-RAN). Details about uncached requested information can be transferred from the cloud to the eRRHs by following both backhaul or fronthaul modes. Bearing in mind the commerce-off between present and future delivery performance, this paper proposes an adaptive selection technique between the 2 delivery modes to reduce the long-time period delivery latency. In this paper, we investigate for the first time the web minimization of the long-time period delivery latency over X-haul links in an F-RAN with time-various and unknown file reputation.<br> |
Diff unifié des changements faits lors de la modification (edit_diff) | @@ -1,1 +1,1 @@
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+<br> Reinforcement Learning (RL) based mostly optimization of on-line caching by assuming a backhaul mode. Signal-to-Noise-Ratio (SNR) efficiency by considering also the optimization of uncoded caching methods. On this work, we study the adaptive number of backhaul and fronthaul transfer modes with the goal of optimizing the efficiency of content material delivery. The backhaul mode permits the switch of knowledge packets from the BBU within the cloud to the eRRHs. The backhaul mode permits the caches of the eRRHs to be updated, which may lower future supply latencies. In contrast, the fronthaul mode allows cooperative C-RAN transmissions which will reduce the present supply latency.<br><br><br> The backhaul mode transfers fractions of the requested files, whereas the fronthaul mode transmits quantized baseband [https://Ufa191Vip.com/ ufa191vip.com] samples as in Cloud-RAN (C-RAN). Details about uncached requested information can be transferred from the cloud to the eRRHs by following both backhaul or fronthaul modes. Bearing in mind the commerce-off between present and future delivery performance, this paper proposes an adaptive selection technique between the 2 delivery modes to reduce the long-time period delivery latency. In this paper, we investigate for the first time the web minimization of the long-time period delivery latency over X-haul links in an F-RAN with time-various and unknown file reputation.<br>
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Lignes ajoutées lors de la modification (added_lines) | <br> Reinforcement Learning (RL) based mostly optimization of on-line caching by assuming a backhaul mode. Signal-to-Noise-Ratio (SNR) efficiency by considering also the optimization of uncoded caching methods. On this work, we study the adaptive number of backhaul and fronthaul transfer modes with the goal of optimizing the efficiency of content material delivery. The backhaul mode permits the switch of knowledge packets from the BBU within the cloud to the eRRHs. The backhaul mode permits the caches of the eRRHs to be updated, which may lower future supply latencies. In contrast, the fronthaul mode allows cooperative C-RAN transmissions which will reduce the present supply latency.<br><br><br> The backhaul mode transfers fractions of the requested files, whereas the fronthaul mode transmits quantized baseband [https://Ufa191Vip.com/ ufa191vip.com] samples as in Cloud-RAN (C-RAN). Details about uncached requested information can be transferred from the cloud to the eRRHs by following both backhaul or fronthaul modes. Bearing in mind the commerce-off between present and future delivery performance, this paper proposes an adaptive selection technique between the 2 delivery modes to reduce the long-time period delivery latency. In this paper, we investigate for the first time the web minimization of the long-time period delivery latency over X-haul links in an F-RAN with time-various and unknown file reputation.<br>
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Horodatage Unix de la modification (timestamp) | 1661414970 |