Real-Time Network Slicing with Uncertain Demand: A Deep Learning Approach

© 2019 IEEE. Practical and efficient network slicing often faces real-time dynamics of network resources and uncertain customer demands. This work provides an optimal and fast resource slicing solution under such dynamics by leveraging the latest advances in deep learning. Specifically, we first introduce a novel system model which allows the network provider to effectively allocate its combinatorial resources, i.e., spectrum, computing, and storage, to various classes of users. To allocate resources to users while taking into account the dynamic demands of users and resources constraints of the network provider, we employ a semi-Markov decision process framework. To obtain the optimal resource allocation policy for the network provider without requiring environment parameters, e.g., uncertain service time and resource demands, a Q-learning algorithm is adopted. Although this algorithm can maximize the revenue of the network provider, its convergence to the optimal policy is particularly slow, especially for problems with large state/action spaces. To overcome this challenge, we propose a novel approach using an advanced deep Q-learning technique, called deep dueling that can achieve the optimal policy at few thousand times faster than that of the conventional Q-learning algorithm. Simulation results show that our proposed framework can improve the long-term average return of the network provider up to 40% compared with other current approaches

Analysis and Implementation of Recovery-Based Discontinuous Galerkin for Diffusion

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76575/1/AIAA-2009-3786-303.pd

Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks

© 2019 IEEE. In this paper, we propose a novel approach to jointly address energy management and network throughput maximization problems for heterogeneous IoT low-power wireless communication networks. In particular, we consider a low-power communication network in which the IoT devices can harvest energy from a dedicated RF energy source to support their transmissions or backscatter the signals of the RF energy source to transmit information to the gateway. Different IoT devices may have dissimilar hardware configurations, and thus they may have various communications types and energy requirements. In addition, the RF energy source may have a limited energy supply source which needs to be minimized. Thus, to maximize the network throughput, we need to jointly optimize energy usage and operation time for the IoT devices under different energy demands and communication constraints. However, this optimization problem is non-convex due to the strong relation between energy supplied by the RF energy source and the IoT communication time, and thus obtaining the optimal solution is intractable. To address this problem, we study the relation between energy supply and communication time, and then transform the non-convex optimization problem to an equivalent convex-optimization problem which can achieve the optimal solution. Through simulation results, we show that our solution can achieve greater network throughputs (up to five times) than those of other conventional methods, e.g., TDMA. In addition, the simulation results also reveal some important information in controlling energy supply and managing low-power IoT devices in heterogeneous wireless communication networks

Fast or Slow: An Autonomous Speed Control Approach for UAV-assisted IoT Data Collection Networks

Unmanned Aerial Vehicles (UAVs) have been emerging as an effective solution for IoT data collection networks thanks to their outstanding flexibility, mobility, and low operation costs. However, due to the limited energy and uncertainty from the data collection process, speed control is one of the most important factors while optimizing the energy usage efficiency and performance for UAV collectors. This work aims to develop a novel autonomous speed control approach to address this issue. To that end, we first formulate the dynamic speed control task of a UAV as a Markov decision process taking into account its energy status and location. In this way, the Q-learning algorithm can be adopted to obtain the optimal speed control policy for the UAV. To further improve the system performance, we develop a highly-effective deep dueling double Q-learning algorithm utilizing outstanding features of the deep neural networks as well as advanced dueling architecture to quickly stabilize the learning process and obtain the optimal policy. Through simulations, we show that our proposed solution can achieve up to 40% greater performance, i.e., an average throughput of the system, compared with other conventional methods. Importantly, the simulation results also reveal significant impacts of UAV’s energy and charging time on the system performance

Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications

This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts: (i) the active-transmit message transmitted by the transmitter using conventional RF signals and (ii) the backscatter message transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios