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Load Balancing and User Association Based on Historical Data

With the rapid increase of demand on mobile data traffic of user equipment (UE), network operators have begun to deploy abundant heterogeneous base stations (BSs) to ensure the quality of service (QoS) of UEs, which will cause new problems such as network congestion and load imbalance. If the pattern of user association (UA) can be adjusted in accordance with the results of traffic prediction, the performance of system will be greatly improved. Therefore, a new neural network approach based on spatial and temporal characteristics of traffic data is proposed for traffic prediction. The fluctuations of traffic in the future week are predicted by the proposed method. Then, UA is represented as a problem of maximizing the utility function of load balancing index, and a dynamic user association based on load prediction algorithm (DUALP) which aims to achieve a proactive load balancing is proposed. The QoS of UEs is ensured and the long-term stability of the system is achieved by DUALP. Experimental results show that compared to the classic UA strategies, the most optimal load distribution is realized by DUALP.