Showing papers by "Zheng Chang published in 2019"

Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach

Abstract: In vehicular networks, in-vehicle user equipment (UE) with limited battery capacity can achieve opportunistic energy saving by offloading energy-hungry workloads to vehicular edge computing nodes via vehicle-to-infrastructure links. However, how to determine the optimal portion of workload to be offloaded based on the dynamic states of energy consumption and latency in local computing, data transmission, workload execution and handover, is still an open issue. In this paper, we study the energy-efficient workload offloading problem and propose a low-complexity distributed solution based on consensus alternating direction method of multipliers. By incorporating a set of local variables for each UE, the original problem, in which the optimization variables of UEs are coupled together, is transformed into an equivalent general consensus problem with separable objectives and constraints. The consensus problem can be further decomposed into a bunch of subproblems, which are distributed across UEs and solved in parallel simultaneously. Finally, the proposed solution is validated based on a realistic road topology of Beijing, China. Simulation results have demonstrated that significant energy saving gain can be achieved by the proposed algorithm.

Propagation Channels for mmWave Vehicular Communications: State-of-the-art and Future Research Directions

Abstract: Vehicular communications essentially support automotive applications for safety and infotainment. For this reason, industry leaders envision an enhanced role for vehicular communications in the fifth generation of mobile communications technology. Over the years, the number of vehicle- mounted sensors has increased steadily, which potentially leads to more volume of critical data communications in a short time. Also, emerging applications such as remote/autonomous driving and infotainment such as high-definition movie streaming require data-rates on the order of multiple Gb/s. Such high data rates require a large system bandwidth, but very limited bandwidth is available in the sub-6 GHz cellular bands. This has sparked research interest in the millimeter wave (mmWave) band (10 GHz-300 GHz), where a large bandwidth is available to support the high data rate and low latency communications envisioned for emerging vehicular applications. However, leveraging mmWave communications requires a thorough understanding of the relevant vehicular propagation channels, which are significantly different from those investigated below 6 GHz. Despite their significance, very few investigations of mmWave vehicular channels are reported in the literature. This work highlights the key attributes of mmWave vehicular communication channels and surveys the recent literature on channel characterization efforts in order to provide a gap analysis and propose possible directions for future research.

Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint

Abstract: Mobile edge computing (MEC) is an important and effective platform to offload the computational services of modern mobile applications, and has gained tremendous attention from various research communities. For delay and resource constrained mobile devices, the important issues include: 1) minimization of the service latency; 2) optimal revenue maximization; and 3) high quality-of-service requirement to offload the computational service offloading. To address the above issues, an adaptive service offloading scheme is designed to provide the maximum revenue and service utilization to MEC. Unlike most of the existing works, we consider both the delay-tolerant and delay-constraint services in order to achieve the optimized service latency and revenue. Furthermore, we consider the different priorities to prioritize the edge services for optimal service offloading. We formulate the proposed scheme mathematically. Simulation results are presented to demonstrate the effectiveness of the proposed adaptive service offloading scheme over other existing state-of-the-art solutions, in terms of service latency, utility value, revenue, and utilization.

Applications of Backscatter Communications for Healthcare Networks

Abstract: Backscatter communication is expected to help in revitalizing the domain of healthcare through its myriad applications. From on-body sensors to in-body implants and miniature embeddable devices, there are many potential use cases that can leverage the miniature and low-powered nature of backscatter devices. However, the existing literature lacks a comprehensive study that provides a distilled review of the latest studies on backscatter communications from the healthcare perspective. Thus, with the objective to promote the utility of backscatter communication in healthcare, this article aims to identify specific applications of backscatter systems. A detailed taxonomy of recent studies and gap analysis for future research directions are provided in this work. Finally, we conduct measurements at 590 MHz in different propagation environments with the in-house designed backscatter device. The link budget results show the promise of backscatter devices to communicate over large distances for indoor environments, which demonstrates its potential in the healthcare system.

Internet of Autonomous Vehicles: Architecture, Features, and Socio-Technological Challenges

Abstract: Mobility is the backbone of urban life and a vital economic factor in the development of the world. Rapid urbanization and the growth of mega-cities are bringing dramatic changes in the capabilities of vehicles. Innovative solutions like autonomy, electrification, and connectivity are on the horizon. How, then, we can provide ubiquitous connectivity to legacy and autonomous vehicles? This article seeks to answer this question by combining recent leaps of innovation in network virtualization with remarkable feats of wireless communications. To do so, this article proposes a novel paradigm called the Internet of Autonomous Vehicles (IoAV). We begin painting the picture of IoAV by discussing the salient features and applications of IoAV, followed by a detailed discussion on the key enabling technologies. Next, we describe the proposed layered architecture of IoAV and uncover some critical functions of each layer. This is followed by the performance evaluation of IoAV, which shows significant advantages of the proposed architecture in terms of transmission time and energy consumption. Finally, to best capture the benefits of IoAV, we enumerate some social and technological challenges, and explain how some unresolved issues can disrupt the widespread use of autonomous vehicles in the future.

Distributed Resource Allocation for Energy Efficiency in OFDMA Multicell Networks With Wireless Power Transfer

Abstract: In this paper, an energy-efficient resource allocation problem is investigated for the wireless power transfer (WPT)-enabled OFDMA multicell networks. In the considered system, multiple base stations (BSs) with a large number of antennas are responsible to provide WPT in the downlink, and the users can recycle and utilize the received energy for uplink data transmission. The role of BS is to execute WPT; thus, there are no data transmissions in the downlink. A time-division protocol is considered to divide the time of downlink WPT and uplink wireless information transfer into separate time slots. With the objective to improve the energy efficiency, we propose the time, subcarrier, and power allocation schemes and antenna selection algorithms. As the perfect channel state information (CSI) is hard to obtain in the practical systems, we also take the case where only estimated CSI is available into consideration when executing resources allocation decisions and analyze the corresponding performance. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem. Then, an efficient alternating direction method of multipliers-based distributed resource allocation algorithm is applied to address the transformed problem. Performance evaluations are conducted to demonstrate the advantages of the proposed schemes.

Internet of Autonomous Vehicles: Architecture, Features, and Socio-Technological Challenges

Abstract: Mobility is the backbone of urban life and a vital economic factor in the development of the world. Rapid urbanization and the growth of mega-cities is bringing dramatic changes in the capabilities of vehicles. Innovative solutions like autonomy, electrification, and connectivity are on the horizon. How, then, we can provide ubiquitous connectivity to the legacy and autonomous vehicles? This paper seeks to answer this question by combining recent leaps of innovation in network virtualization with remarkable feats of wireless communications. To do so, this paper proposes a novel paradigm called the Internet of autonomous vehicles (IoAV). We begin painting the picture of IoAV by discussing the salient features, and applications of IoAV which is followed by a detailed discussion on the key enabling technologies. Next, we describe the proposed layered architecture of IoAV and uncover some critical functions of each layer. This is followed by the performance evaluation of IoAV which shows the significant advantage of the proposed architecture in terms of transmission time and energy consumption. Finally, to best capture the benefits of IoAV, we enumerate some social and technological challenges and explain how some unresolved issues can disrupt the widespread use of autonomous vehicles in the future.

Secrecy Analysis and Learning-Based Optimization of Cooperative NOMA SWIPT Systems

Abstract: Non-orthogonal multiple access (NOMA) is considered to be one of the best candidates for future networks due to its ability to serve multiple users using the same resource block. Although early studies have focused on transmission reliability and energy efficiency, recent works are considering cooperation among the nodes. The cooperative NOMA techniques allow the user with a better channel (near user) to act as a relay between the source and the user experiencing poor channel (far user). This paper considers the link security aspect of energy harvesting cooperative NOMA users. In particular, the near user applies the decode-and-forward (DF) protocol for relaying the message of the source node to the far user in the presence of an eavesdropper. Moreover, we consider that all the devices use power-splitting architecture for energy harvesting and information decoding. We derive the analytical expression of intercept probability. Next, we employ deep learning based optimization to find the optimal power allocation factor. The results show the robustness and superiority of deep learning optimization over conventional iterative search algorithm.

Multi-Hop D2D Communications With Network Coding: From a Performance Perspective

Abstract: Multi-hop device-to-device (D2D) communications play an important role in expanding D2D coverage. In this paper, we study a relay-based and network-coding-assisted (in particular, XOR coding) multi-hop D2D communication system. In the system, toward jointly considering the impact of interference and network traffic conditions on the quality of D2D communications, various channel fading models and traffic models are investigated, and the packet loss probability of D2D links is meticulously computed using these models. With the packet loss probability of D2D links, the general closed-form expressions of end-to-end packet loss probability (E2EPLP) of the system with the presence (or absence) of XOR coding are subsequently derived. Our experiments reveal that both the number of relays and the mechanism of XOR coding can affect the system performance. Specifically, the increase in the number of relays will lower the overall system performance (e.g., an increase in the E2EPLP and end-to-end completion time, and a decrease of the end-to-end rate may follow as a result). On the other hand, although the presence of XOR coding unfortunately raises the system E2EPLP, it can effectively improve the end-to-end completion time and end-to-end rate. It is our belief that the analytical approach proposed in this paper and the results found in our work can be useful to peer studies in the context of applying network coding in multi-hop D2D networks.

Joint optimization of energy and delay for computation offloading in cloudlet-assisted mobile cloud computing

Abstract: In the mobile cloud computing (MCC), although offloading requests to the distant central cloud or nearby cloudlet can reduce energy consumption at the mobile devices (MDs), it may also incur a large execution delay including transmission time from the MDs to the servers and waiting time at the servers. Therefore, how to balance the energy consumption and delay performance is of great research importance. In this paper, we bring a thorough study on the energy consumption and execution delay of offloading process in a cloudlet-assisted MCC. Specifically, heterogeneity of request executions are explicitly considered. When there is a small cell base station (SBS) available, the MDs can connect with cloudlet via the SBS and if only a macro cell base station is available, the MD can connect with the central cloud through it. We derive the analytic results of the energy consumption and execution delay performance with the assumption of three different queue models at the MD, cloudlet and central cloud. Based on the theoretical analysis, the multi-objective optimization problems are formulated with the joint objectives to minimize the energy consumption and delay by finding the optimal offloading probability. The simulation results demonstrate the effectiveness of the proposed scheme.

Applications of Backscatter Communications for Healthcare Networks

Abstract: Backscatter communication is expected to help in revitalizing the domain of healthcare through its myriad applications. From on-body sensors to in-body implants and miniature embeddable devices, there are many potential use cases that can leverage the miniature and low-powered nature of backscatter devices. However, the existing literature lacks a comprehensive study that provides a distilled review of the latest studies on backscatter communications from the healthcare perspective. Thus, with the objective to promote the utility of backscatter communication in healthcare, this paper aims to identify specific applications of backscatter systems. A detailed taxonomy of recent studies and gap analysis for future research directions are provided in this work. Finally, we conduct measurements at 590 MHz in different propagation environments with the in-house designed backscatter device. The link budget results show the promise of backscatter devices to communicate over large distances for indoor environments which demonstrates its potential in the healthcare system.

Secrecy Analysis and Learning-based Optimization of Cooperative NOMA SWIPT Systems

Abstract: Non-orthogonal multiple access (NOMA) is considered to be one of the best candidates for future networks due to its ability to serve multiple users using the same resource block. Although early studies have focused on transmission reliability and energy efficiency, recent works are considering cooperation among the nodes. The cooperative NOMA techniques allow the user with a better channel (near user) to act as a relay between the source and the user experiencing poor channel (far user). This paper considers the link security aspect of energy harvesting cooperative NOMA users. In particular, the near user applies the decode-and-forward (DF) protocol for relaying the message of the source node to the far user in the presence of an eavesdropper. Moreover, we consider that all the devices use power-splitting architecture for energy harvesting and information decoding. We derive the analytical expression of intercept probability. Next, we employ deep learning based optimization to find the optimal power allocation factor. The results show the robustness and superiority of deep learning optimization over conventional iterative search algorithm.

A dynamic unloading method of fog computation based on Lyapunov optimization

Abstract: The invention provides a dynamic unloading method of fog calculation based on optimization, belonging to the field of wireless network communication. The present invention divides the calculation request into a local calculation portion and a fog calculation portion. By offloading computationally intensive requests to fog nodes, application performance can be significantly improved. An online joint radio and computational resource algorithm based on Lyapunov optimization is used to derive the upper bound of Lyapunov migration penalty function. By minimizing the upper bound from the perspectiveof different decision variables, , the optimal CPU cycle frequency of local processor is obtained with a convex optimization method. By using the predefined offload priority function, the optimal transmission power of the optimal subchannel is obtained. On the fog node, the optimal request scheduling decision is obtained by absurdity proof.

Throughput Maximization of Low-Latency Communication with Imperfect CSI in Finite Blocklength Regime

Abstract: We consider a low-latency communication network operating with finite blocklength (FBL) codes. During the transmission, the minimum mean squared error (MMSE) channel estimation is assumed to be applied to obtain the instantaneous but imperfect Channel State Information (CSI) for the rate selection. We aim at optimizing the FBL throughput of the system under given reliability constraints. First, we provide an optimal frame structure design by optimally allocating the total frame length for MMSE training of channel estimation and data transmission. In addition, we further improve the FBL throughput considering channel dynamics which optimally selects the coding rate per frame. Combining the frame structure and the coding rate selection, a joint optimization problem is studied and solved by a sub-optimal algorithm. In the simulation study, we validate the proposed analytical model and evaluate the FBL throughput of the proposed solution in comparison to benchmark schemes.

Collaborative Content Downloading in VANETs with Fuzzy Comprehensive Evaluation

Abstract: Vehicle collaborative content downloading has become a hotspot in current vehicular ad-hoc network (VANET) research However, in reality, the highly dynamic nature of VANET makes users lose resources easily, and the transmission of invalid segment data also wastes valuable bandwidth and storage of the users’ vehicles In addition, the individual need of each customer vehicle should also be taken into consideration when selecting an agent vehicle for downloading In this paper, a novel scheme is proposed for vehicle selection in the download of cooperative content from the Internet, by considering the basic evaluation information of the vehicle To maximize the overall throughput of the system, a collaborative content downloading algorithm is proposed, which is based on fuzzy evaluation and a customer’s own expectations, in order to solve the problems of agent vehicle selection With the premise of ensuring successful downloading and the selection preferences of customer vehicles, linear programming is used to optimize the distribution of agent vehicles and maximize customer’s satisfaction Simulation results show that the proposed scheme works well in terms of average quality of service, average bandwidth efficiency, failure frequency, and average consumption

Multi-Resource Management for Multi-Tier Space Information Networks: A Cooperative Game

Abstract: With the drastic increase of space information network (SIN) traffic and the diversity of network traffic types, the optimal allocation of the scarce network resources is of great significance for optimizing the SIN system capability. In this paper, we propose a multi-resource management method for multi-tier SIN using the cooperative Nash bargaining solution. Since the original problem is a non-convex problem, we firstly make logarithmic transition, and then find a tightest lower bound function to convert the initial problem into a convex one. In order to carry out the optimal bandwidth and power allocation in SIN, we construct a joint bandwidth and power allocation (JBPA) algorithm. Simulation results show the performance improvement of the JBPA scheme and the convergence of JBPA algorithm.

Optimal Buffer Resource Allocation in Wireless Caching Networks

Abstract: Wireless caching systems have been exhaustively investigated in recent years. Due to limited buffer capacity, and unbalanced arrival and service rates, the backlogs may exist in the caching node and even cause buffer overflow. In this paper, we first investigate the relationship among backlogs, buffer capacity, data arrival rate and service rate, utilizing the martingale theory which is flexible in handling any arrival and service processes. Then given a target buffer overflow probability, the minimal required buffer portion is determined. If the devoted buffer capacity can fulfill all serving users' minimal buffer requirements, an optimization problem is constructed with the objective to minimize the overall buffer overflow probability. The optimization solution is obtained by a modified waterfilling scheme. Finally, the numerical results are illustrated to demonstrate the superiority of the proposed scheme.