Study on Based Reinforcement Q-Learning for Mobile Load Balancing Techniques in LTE-A HetNets
作者： Juanxiong Xu , Lun Tang , Qianbin Chen , Li Yi
DOI: 10.1109/CSE.2014.324
关键词:
摘要: Mobile-broadband traffic has experienced a large increase and the network continuously expanded over past few years. Pico cells are envisioned to cope with such demand of capacity in environments. Since those small low-cost nodes, thorough deployment is not typically performed, particularly LTE-A Het Nets. As result, matching between resources rarely optimal. In this paper, several common load balancing algorithms studied compared solve localized congestion problems. particular, these techniques implemented by reinforcement Q-Learning algorithm that forecasts status for every node, combined related concepts self-organization which current research focus adaptive parameters so improve performance.
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