User Association for Load Balancing in Vehicular Networks: An Online Reinforcement Learning Approach

被引:81
作者
Li, Zhong [1 ]
Wang, Cheng [2 ,3 ,4 ]
Jiang, Chang-Jun [2 ,3 ,4 ]
机构
[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China
[2] Tongji Univ, Dept Comp Sci, Shanghai 201804, Peoples R China
[3] Tongji Univ, Key Lab Embedded Syst & Serv Comp, Minist Educ, Shanghai 10014176, Peoples R China
[4] Shanghai Elect Transact & Informat Serv Collabora, Shanghai 200000, Peoples R China
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
User association; online reinforcement learning; load balancing; vehicular networks; HETEROGENEOUS CELLULAR NETWORKS; WIRELESS NETWORKS; DOWNLINK; SYSTEMS; HETNETS; WLANS;
D O I
10.1109/TITS.2017.2709462
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Recently, a number of technologies have been developed to promote vehicular networks. When vehicles are associated with the heterogeneous base stations (e.g., macrocells, picocells, and femtocells), one of the most important problems is to make load balancing among these base stations. Different from common mobile networks, data traffic in vehicular networks can be observed having regularities in the spatial-temporal dimension due to the periodicity of urban traffic flow. By taking advantage of this feature, we propose an online reinforcement learning approach, called ORLA. It is a distributed user association algorithm for network load balancing in vehicular networks. Based on the historical association experiences, ORLA can obtain a good association solution through learning from the dynamic vehicular environment continually. In the long run, the real-time feedback and the regular traffic association patterns both help ORLA cope with the dynamics of network well. In experiments, we use QiangSheng taxi movement to evaluate the performance of ORLA. Our experiments verify that ORLA has higher quality load balancing compared with other popular association methods.
引用
收藏
页码:2217 / 2228
页数:12
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