On the Application of Q-learning for Mobility Load Balancing in Realistic Vehicular Scenarios
Author:
Affiliation:
1. i2CAT Foundation,Barcelona,Spain,08034
2. Universitat Politècnica de Catalunya (UPC),Signal Theory and Communications Dept.,Barcelona,Spain,08034
Funder
Ministry of Economy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10199114/10199145/10201091.pdf?arnumber=10201091
Reference31 articles.
1. Deep Reinforcement Learning-based CIO and Energy Control for LTE Mobility Load Balancing
2. Decentralized Deep Reinforcement Learning Meets Mobility Load Balancing;chang;IEEE Transactions on Networking,2022
3. User Association for Load Balancing in Vehicular Networks: An Online Reinforcement Learning Approach
4. Load Balancing and QoS-Aware Network Selection Scheme in Heterogeneous Vehicular Networks
5. Understanding the Impact of the PC5 Resource Grid Design on the Capacity and Efficiency of LTE-V2X in Vehicular Networks
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