Electrical conduction processes in ZnO in a wide temperature range 20--500 K

We have investigated the electrical conduction processes in as-grown and thermally cycled ZnO single crystal as well as as-grown ZnO polycrystalline films over the wide temperature range 20--500 K. In the case of ZnO single crystal between 110 and 500 K, two types of thermal activation conduction processes are observed. This is explained in terms of the existence of both shallow donors and intermediately deep donors which are consecutively excited to the conduction band as the temperature increases. By measuring the resistivity $\rho(T)$ of a given single crystal after repeated thermal cycling in vacuum, we demonstrate that oxygen vacancies play an important role in governing the shallow donor concentrations but leave the activation energy ($\simeq27\pm$2 meV) largely intact. In the case of polycrystalline films, two types of thermal activation conduction processes are also observed between $\sim$150 and 500 K. Below $\sim$150 K, we found an additional conduction process due to the nearest-neighbor-hopping conduction mechanism which takes place in the shallow impurity band. As the temperatures further decreases below $\sim$80 K, a crossover to the Mott variable-range-hopping conduction process is observed. Taken together with our previous measurements on $\rho (T)$ of ZnO polycrystalline films in the temperature range 2--100 K [Y. L. Huang {\it et al.}, J. Appl. Phys. \textbf{107}, 063715 (2010)], this work establishes a quite complete picture of the overall electrical conduction mechanisms in the ZnO material from liquid-helium temperatures up to 500 K.Comment: 8 pages, 4 figures, 3 table

Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

Backpropagation-Based Cooperative Localization of Primary User for Avoiding Hidden-Node Problem in Cognitive Networks

Cognitive radio (CR) is a technology to implement opportunistic spectrum sharing to improve the spectrum utilization. However, there exists a hidden-node problem, which can be a big challenge to solve especially when the primary receiver is passive listening. We aim to provide a solution to the hidden-node problem for passive-listening receiver based on cooperation of multiple CRs. Specifically, we consider a cooperative GPS-enabled cognitive network. Once the existence of PU is detected, a localization algorithm will be employed to first estimate the path loss model for the environment based on backpropagation method and then to locate the position of PU. Finally, a disable region is identified taking into account the communication range of both the PU and the CR. The CRs within the disabled region are prohibited to transmit in order to avoid interfering with the primary receiver. Both analysis and simulation results are provided