A DQN-based model for intelligent network selection in heterogeneous wireless systems
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914241145995264 |
|---|---|
| author | Bendaoud, Fayssal Amraoui, Asma Sehimi, karim |
| author_facet | Bendaoud, Fayssal Amraoui, Asma Sehimi, karim |
| contents | Wireless communications have been at the center of the revolution in technology for the last few years. The 5G communication system is the pinnacle of these technologies; however 4G LTE, WiFi, and even satellite technologies are still employed worldwide. So, the aim of the next generation network is to take advantage of these technologies for the better of the end users. Our research analyzes this subject and reveals a new and intelligent method that allows users to select the suitable RAT at each time and, therefore, to switch to another RAT if necessary. The Deep Q Network DQN algorithm was utilized, which is a reinforcement learning algorithm that determines judgments based on antecedent actions (rewards and punishments). The approach exhibits a high accuracy, reaching 93 percent, especially after a given number of epochs (the exploration phase), compared to typical MADM methods where the accuracy does not exceed 75 percent |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04978 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A DQN-based model for intelligent network selection in heterogeneous wireless systems Bendaoud, Fayssal Amraoui, Asma Sehimi, karim Networking and Internet Architecture Wireless communications have been at the center of the revolution in technology for the last few years. The 5G communication system is the pinnacle of these technologies; however 4G LTE, WiFi, and even satellite technologies are still employed worldwide. So, the aim of the next generation network is to take advantage of these technologies for the better of the end users. Our research analyzes this subject and reveals a new and intelligent method that allows users to select the suitable RAT at each time and, therefore, to switch to another RAT if necessary. The Deep Q Network DQN algorithm was utilized, which is a reinforcement learning algorithm that determines judgments based on antecedent actions (rewards and punishments). The approach exhibits a high accuracy, reaching 93 percent, especially after a given number of epochs (the exploration phase), compared to typical MADM methods where the accuracy does not exceed 75 percent |
| title | A DQN-based model for intelligent network selection in heterogeneous wireless systems |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2601.04978 |