Resource allocation in clustered M2M networks: a q-learning approach
Notice bibliographique
Résumé
Machine to machine (M2M) communication has received increasing attention in recent years. A M2M network exhibits salient features such as large number of machines/devices, low data rates, delay tolerant/sensitive, small sized packets, energy-constrained and low or no mobility. A large number of M2M terminals may exist in a small area with many trying to simultaneously and randomly access for channel resources - which will result in overload and access problem. This increased signaling overhead and diverse requirements of machine type communication devices (MTCDs) call for the development of flexible and efficient scheduling and random access techniques. In this thesis, we first review and compare various scheduling and random access techniques in LTE-based cellular networks for M2M communication. We also discuss how successful they are to fulfill the unique requirements of M2M communication and networking. Resource management in M2M networks with a large number devices is also reviewed from the access point of view. We propose a multi-objective optimization based solution to the problem of resource allocation in interference-limited M2M communication. We consider MTCDs in a clustered network structure, where they are divided into clusters and the devices belonging to a cluster communicate to cluster head (or controller). We maximize the number of admitted MTCD controllers and throughput with least interference caused to conventional primary users. We formulate the problem as a mixed-integer non-linear problem with multiple objectives and solve it using meshed adaptive direct search (MADS) algorithm. Simulation results show the effects of varying different parameters on cumulative throughput and the number of admitted iii MTCD controllers. We then formulate the slot selection problem in M2M networks with admitted MTCDs as an optimization problem. We present a solution using the Q-learning algorithm to select conflict-free slot assignment in a random access network with MTCD controllers. The performance of the solution is dependent on parameters such as learning rate and reward. We thoroughly analyze the performance of the proposed algorithm considering different parameters related to its operation. We also compare it with simple ALOHA and channel-based scheduled allocation and show that the proposed Q-learning based technique has a higher probability of assigning slots compared to these techniques. We then present a block based Q-learning algorithm for the scheduling of MTCDs in clustered M2M communication networks. At first centralized slot assignment is done and an algorithm is proposed for minimizing the inter-cluster interference. Then we propose to use an Q-learning algorithm to assign slots in a distributed manner and comparison is made between the two schemes. Afterwards, we show the effects of distributed slot-assignment with respect to varying signal-to-interference ratio on convergence rate and convergence probability. Cumulative distribution function is used to study the effect of various SIR threshold levels on the convergence probability. With the increase in SIR threshold levels, increase in convergence time and decrease in convergence probability are observed, as less block configuration fulfills the required threshold in the M2M network.
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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».