Network Selection Algorithm Based on Improved Deep Q-Learning

In ultra dense heterogeneous wireless network with sleep mechanism, in view of the problem that the network dynamic is enhanced and the handoff performance is reduced, a network selection algorithm based on improved deep Q-learning is proposed. Firstly, according to the dynamic analysis of the network, a deep Q-learning network selection model is constructed; Secondly, the training samples and weights of the offline training module in deep Q-learning network selection model, which are transferred to the online network decision-making module through the transfer learning; Finally, the training samples and weights of transfer are used to accelerate the process of training neural network, and the optimal network selection strategy is obtained. Experimental results demonstrate that the proposed algorithm improves significantly the performance degradation of high dynamic network handoff caused by sleep mechanism and the time complexity of traditional deep Q-learning algorithm for online network selection.