Distributed Stochastic Power Control in Ad-hoc Networks: A Nonconvex Case

Utility-based power allocation in wireless ad-hoc networks is inherently nonconvex because of the global coupling induced by the co-channel interference. To tackle this challenge, we first show that the globally optimal point lies on the boundary of the feasible region, which is utilized as a basis to transform the utility maximization problem into an equivalent max-min problem with more structure. By using extended duality theory, penalty multipliers are introduced for penalizing the constraint violations, and the minimum weighted utility maximization problem is then decomposed into subproblems for individual users to devise a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the total utility by simulated annealing. The proposed distributed power control algorithm can guarantee global optimality at the cost of slow convergence due to simulated annealing involved in the global optimization. The geometric cooling scheme and suitable penalty parameters are used to improve the convergence rate. Next, by integrating the stochastic power control approach with the back-pressure algorithm, we develop a joint scheduling and power allocation policy to stabilize the queueing systems. Finally, we generalize the above distributed power control algorithms to multicast communications, and show their global optimality for multicast traffic.Comment: Contains 12 pages, 10 figures, and 2 tables; work submitted to IEEE Transactions on Mobile Computin

A Facile Method to Construct MXene/CuO Nanocomposite with Enhanced Catalytic Activity of CuO on Thermal Decomposition of Ammonium Perchlorate

In this work, a mixing-calcination method was developed to facilely construct MXene/CuO nanocomposite. CuO and MXene were first dispersed in ethanol with sufficient mixing. After solvent evaporation, the dried mixture was calcinated under argon to produce a MXene/CuO nanocomposite. As characterized by X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), and X-ray photoelectron spectra (XPS), CuO nanoparticles (60⁻100 nm) were uniformly distributed on the surface and edge of MXene nanosheets. Furthermore, as evaluated by differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA), the high-temperature decomposition (HTD) temperature decrease of ammonium perchlorate (AP) upon addition of 1 wt% CuO (hybridized with 1 wt% MXene) was comparable with that of 2 wt% CuO alone, suggesting an enhanced catalytic activity of CuO on thermal decomposition of AP upon hybridization with MXene nanosheets. This strategy could be further applied to construct other MXene/transition metal oxide (MXene/TMO) composites with improved performance for various applications

Intelligent edge:leveraging deep imitation learning for mobile edge computation offloading

Abstract In this work, we propose a new deep imitation learning (DIL)-driven edge-cloud computation offloading framework for MEC networks. A key objective for the framework is to minimize the offloading cost in time-varying network environments through optimal behavioral cloning. Specifically, we first introduce our computation offloading model for MEC in detail. Then we make fine-grained offloading decisions for a mobile device, and the problem is formulated as a multi-label classification problem, with local execution cost and remote network resource usage consideration. To minimize the offloading cost, we train our decision making engine by leveraging the deep imitation learning method, and further evaluate its performance through an extensive numerical study. Simulation results show that our proposal outperforms other benchmark policies in offloading accuracy and offloading cost reduction. At last, we discuss the directions and advantages of applying deep learning methods to multiple MEC research areas, including edge data analytics, dynamic resource allocation, security, and privacy, respectively

IEEE Access special section editorial:recent advances in socially-aware mobile networking

Abstract Mobile data traffic has been growing exponentially over the past few years. A report from Cisco shows that the mobile data traffic in 2014 grew 69 percent and was nearly 30 times the size of the entire global Internet in 2000 [item 1) in the Appendix]. One of the primary contributors to the explosive mobile traffic growth is the rapid proliferation of mobile social applications running on multimedia mobile devices (particularly smartphones). These sharp increases in mobile traffic (particularly from mobile social applications) are projected to continue in the foreseeable future. As mobile networks by and large are designed and deployed to meet people’s social needs, people’s behaviors and interactions in the social domain will shape their ways to access mobile services. Therefore, there is an urgent need to integrate social effects into the design of mobile networks