QoS-Aware Link Adaptation for Beyond 5G Networks: A Deep Reinforcement Learning Approach
Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack th...
Saved in:
| Main Authors: | Ali Parsa, Neda Moghim, Sachin Shetty |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11104833/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-Link Fragmentation-Aware Deep Reinforcement Learning RSA Algorithm in Elastic Optical Network
by: Jing Jiang, et al.
Published: (2025-06-01) -
Complexification through gradual involvement and reward Providing in deep reinforcement learning
by: E. V. Rulko,
Published: (2024-12-01) -
Reinforcement Learning Based Acceptance Criteria for Metaheuristic Algorithms
by: Oğuzhan Ahmet Arık, et al.
Published: (2025-08-01) -
Deep Reinforcement Learning-Based Computation Offloading in UAV Swarm-Enabled Edge Computing for Surveillance Applications
by: S. M. Asiful Huda, et al.
Published: (2023-01-01) -
Reinforcement-Learning-Based Edge Offloading Orchestration in Computing Continuum
by: Ioana Ramona Martin, et al.
Published: (2024-11-01)