Age of Information of Multi-user Mobile Edge Computing Systems

In this paper, we analyze the average age of information (AoI) and the average peak AoI (PAoI) of a multiuser mobile edge computing (MEC) system where a base station (BS) generates and transmits computation-intensive packets to user equipments (UEs). In this MEC system, we focus on three computing schemes: (i) The local computing scheme where all computational tasks are computed by the local server at the UE, (ii) The edge computing scheme where all computational tasks are computed by the edge server at the BS, and (iii) The partial computing scheme where computational tasks are partially allocated at the edge server and the rest are computed by the local server. Considering exponentially distributed transmission time and computation time and adopting the first come first serve (FCFS) queuing policy, we derive closed-form expressions for the average AoI and average PAoI. To address the complexity of the average AoI expression, we derive simple upper and lower bounds on the average AoI, which allow us to explicitly examine the dependence of the optimal offloading decision on the MEC system parameters. Aided by simulation results, we verify our analysis and illustrate the impact of system parameters on the AoI performance

Detailed quantitative description of fluvial reservoirs: A case study of L6-3 Layer of Sandgroup 6 in the second member of Shahejie Formation, Shengtuo Oilfield, China

 The steady development of the oil field is facing severe challenges due to the problems of small-layer division, unclear genesis period and unclear river channel distribution in the 4-6 sand formation in the second district of Shengtuo Oilfield. Based on the processing and optimization of logging data, this paper firstly divided the isochronous strata and established the high-resolution isochronous stratigraphic framework. Using the geo-statistics method in the stratigraphic framework, the sand bodies in each small layer were divided according to the principle of equal time of fluvial facies. On this basis, the distribution pattern of the sand bodies in each stage was simulated by the magnetic random walk model. The magnetic random walk model has obtained robust simulation results, which is consistent with the anatomy of reservoir architectures by experienced geologists. The results also show that the number of channels in each small-layer is different, while the overall distribution of NE direction is reflected. At present, the model can well simulate the position of the main channel line, but it cannot reflect the variation of the river width. The method of quantitative fine description based on logging data has great potential application in fluvial reservoir, especially the magnetic random walk model that can reveal the distribution of sand body in every stage. At the same time, the model can also reflect certain randomness and facilitate the uncertainty analysis of geological factors.Cited as: Li, J., Yan, K., Ren, H., Sun, Z. Detailed quantitative description of fluvial reservoirs: A case study of L6-3 Layer of Sandgroup 6 in the second member of Shahejie Formation, Shengtuo Oilfifield, China. Advances in Geo-Energy Research, 2020, 4(1): 43-53, doi: 10.26804/ager.2020.01.0

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

A Survey on Causal Reinforcement Learning

While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov Decision Process (MDP), Partially Observed Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment Regime (DTR). Moreover, we summarize the evaluation matrices and open sources while we discuss emerging applications, along with promising prospects for the future development of CRL.Comment: 29 pages, 20 figure

Evidence for critical scaling of plasmonic modes at the percolation threshold in metallic nanostructures

In this work we provide the experimental demonstration of critical scaling of plasmonic resonances in a percolation series of periodic structures which evolve from arrays of holes to arrays of quasi-triangles. Our observations are in agreement with the general percolation theory and could lead to sensor and detector applications