A Turbo Q-Learning (TQL) for Energy Efficiency Optimization in Heterogeneous Networks

Abstract

In order to maximize energy efficiency in heterogeneous networks (HetNets), a turbo Q-Learning (TQL) combined with multistage decision process and tabular Q-Learning is proposed to optimize the resource configuration. For the large dimensions of action space, the problem of energy efficiency optimization is designed as a multistage decision process in this paper, according to the resource allocation of optimization objectives, the initial problem is divided into several subproblems which are solved by tabular Q-Learning, and the traditional exponential increasing size of action space is decomposed into linear increase. By iterating the solutions of subproblems, the initial problem is solved. The simple stability analysis of the algorithm is given in this paper. As to the large dimension of state space, we use a deep neural network (DNN) to classify states where the optimization policy of novel Q-Learning is set to label samples. Thus far, the dimensions of action and state space have been solved. The simulation results show that our approach is convergent, improves the convergence speed by 60% while maintaining almost the same energy efficiency and having the characteristics of system adjustment.

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

“…APPs in the general world enter the monitor mode using a subset of Secure Monitor Call instructions or HW exception mechanisms. TrustZone technology is widely used for its high efficiency, lightweightness, and security, which can be deployed in heterogeneous mobile systems [4,5] and smart homes [6] , such as Qualcomm SEE architecture, Kinibi microkernel, Trusty, and open-source (OP)-TEE. Among them, OP-TEE is an open-source implementation of TEE developed by STMicroelectronics and Linaro.…”