RESEARCH OF 5G HUDN NETWORK SELECTION ALGORITHM BASED ON DUELING-DDQN.

Abstract

Due to the dense deployment and the diversity of user service types in the 5G
HUDN environment, a more fexible network selection algorithm is required to reduce
the network blocking rate and improve the user’s quality of service (QoS). Considering
the QoS requirements and preferences of the users, a network selection algorithm based
on Dueling-DDQN is proposed by using deep reinforcement learning. Firstly, the state,
action space and reward function of the user- selected network are designed. Then, by
calculating the network selection benefts for diferent types of services initiated by
users, the analytic hierarchy process is used to establish the weight relationship
between the diferent user services and the network attributes. Finally, a deep Q neural
network is used to solve and optimize the proposed target and obtain the user’s best
network selection strategy and long term network selection benefts. The simulation
results show that compared with other algorithms, the proposed algorithm can
effectively reduce the network blocking rate while reducing the switching time.