A Hybrid Proactive Caching System in Vehicular Networks Based on Contextual Multi-Armed Bandit Learning
Proactive edge caching has been regarded as an effective approach to satisfy user experience in mobile networks by providing seamless content transmission and reducing network delay. This is particularly useful in rapidly changing vehicular networks. This paper addresses the proactive edge caching (...
Saved in:
| Main Authors: | Qiao Wang, David Grace |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10077392/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evolutionary Algorithms for Edge Server Placement in Vehicular Edge Computing
by: A. Surayya, et al.
Published: (2025-01-01) -
SPARCQ: Enhancing Scalability and Adaptability of Proactive Edge Caching Through Q-Learning
by: Shruti Lall, et al.
Published: (2025-01-01) -
Optimizing proactive content caching with mobility aware deep reinforcement & asynchronous federate learning in VEC
by: Afsana Kabir Sinthia, et al.
Published: (2025-04-01) -
Adaptive Noise Exploration for Neural Contextual Multi-Armed Bandits
by: Chi Wang, et al.
Published: (2025-01-01) -
Gaussian Process with Vine Copula-Based Context Modeling for Contextual Multi-Armed Bandits
by: Jong-Min Kim
Published: (2025-06-01)