TY - JOUR
AU - Zheng, Gan
AB - In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instanta- neously. Numerical results demonstrate the great approximation to the optimum and generalization ability. Keywords DNN  HetNets  Joint optimization  User cluster  Content placement 1 Introduction popular files dominate the requests of the users. Inspired by this fact, cache is introduced to reduce the duplicate file The explosive data growth in online social network has transmission. Beside the optimal cache strategy to improve 
TI - Deep learning-based edge caching for multi-cluster heterogeneous networks
JF - Neural Computing and Applications
DO - 10.1007/s00521-019-04040-z
DA - 2019-02-18
UR - https://www.deepdyve.com/lp/springer-journals/deep-learning-based-edge-caching-for-multi-cluster-heterogeneous-2CdTfeJsF2
SP - 1
EP - 12
VL - OnlineFirst
IS - 
DP - DeepDyve
ER -