The Experts below are selected from a list of 2931 Experts worldwide ranked by ideXlab platform
Tony Q. S. Quek - One of the best experts on this subject based on the ideXlab platform.
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Joint Uplink and Downlink Resource Configuration for Ultra-reliable and Low-latency Communications
IEEE Transactions on Communications, 2018Co-Authors: Changyang She, Chenyang Yang, Tony Q. S. QuekAbstract:Supporting ultra-reliable and low-latency communications (URLLC) is one of the major goals for the fifth-generation cellular networks. Since spectrum usage efficiency is always a concern, and large bandwidth is required for ensuring stringent quality-of-service (QoS), we minimize the total bandwidth under the QoS constraints of URLLC. We first propose a packet delivery mechanism for URLLC. To reduce the required bandwidth for ensuring queueing delay, we consider a statistical multiplexing queueing mode, where the packets to be sent to different devices are waiting in one queue at the base station, and broadcast mode is adopted in downlink transmission. In this way, downlink bandwidth is shared among packets of multiple devices. In uplink transmission, different subchannels are allocated to different devices to avoid strong interference. Then, we jointly optimize uplink and downlink bandwidth Configuration and delay components to minimize the total bandwidth required to guarantee the overall packet loss and end-to-end delay, which includes uplink and downlink transmission delays, queueing delay and backhaul delay. We propose a two-step method to find the optimal solution. Simulation and numerical results validate our analysis and show remarkable performance gain by jointly optimizing uplink and downlink Configuration.
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Joint Uplink and Downlink Resource Configuration for Ultra-Reliable and Low-Latency Communications
IEEE Transactions on Communications, 2018Co-Authors: Chenyang Yang, Tony Q. S. QuekAbstract:Supporting ultra-reliable and low-latency communications (URLLC) is one of the major goals for the fifth-generation cellular networks. Since spectrum usage efficiency is always a concern, and large bandwidth is required for ensuring stringent quality-of-service (QoS), we minimize the total bandwidth under the QoS constraints of URLLC. We first propose a packet delivery mechanism for URLLC. To reduce the required bandwidth for ensuring queueing delay, we consider a statistical multiplexing queueing mode, where the packets to be sent to different devices are waiting in one queue at the base station, and broadcast mode is adopted in downlink transmission. In this way, downlink bandwidth is shared among packets of multiple devices. In uplink transmission, orthogonal subchannels are allocated to different devices to avoid strong interference. Then, we jointly optimize uplink and downlink bandwidth Configuration and delay components to minimize the total bandwidth required to guarantee the overall packet loss and end-to-end delay, which includes uplink and downlink transmission delays, queueing delay, and backhaul delay. We propose a two-step method to find the optimal solution. Simulation and numerical results validate our analysis and show remarkable performance gain by jointly optimizing uplink and downlink Configuration.
Jianfeng Lv - One of the best experts on this subject based on the ideXlab platform.
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WiMob - Smart Resource Configuration and Task Offloading with Ultra-Dense Edge Computing
2019 International Conference on Wireless and Mobile Computing Networking and Communications (WiMob), 2019Co-Authors: Jianfeng LvAbstract:The ongoing increasing number of mobile devices (MDs) with innovative applications yields unprecedented demands for user experience and network capacity expansion. The combination of ultra-dense network (UDN) and mobile edge computing (MEC) has been envisioned as a promising futuristic technology. It can remarkably improve the capacity of system and extend cloud-computing capabilities to proximate edge servers, by which the growing computation requirements of MDs will be met. However, what is not addressed well is how to optimize the Configuration of computing Resources with varying computation demands of MDs being satisfied, aiming at maximizing the operating earnings of operators while decreasing the cost of MDs. Considering the diverse computation demands in different regions and variational computing Resources of edge servers, it is hard to solve this problem by traditional methods. To address this issue, an effective solution is generating an optimal computing Resource Configuration strategy and task offloading profile in time-varying UDN scenarios. Toward this end, a deep Q-network based scheme is proposed to achieve maximum long-term weighted network utility in such ever-changing environments. Simulation results validate the significant performance improvement of our scheme in weighted network utility and task offloading compared to conventional game-theoretical solution.
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Smart Resource Configuration and Task Offloading with Ultra-Dense Edge Computing
2019 International Conference on Wireless and Mobile Computing Networking and Communications (WiMob), 2019Co-Authors: Jianfeng LvAbstract:The ongoing increasing number of mobile devices (MDs) with innovative applications yields unprecedented demands for user experience and network capacity expansion. The combination of ultra-dense network (UDN) and mobile edge computing (MEC) has been envisioned as a promising futuristic technology. It can remarkably improve the capacity of system and extend cloud-computing capabilities to proximate edge servers, by which the growing computation requirements of MDs will be met. However, what is not addressed well is how to optimize the Configuration of computing Resources with varying computation demands of MDs being satisfied, aiming at maximizing the operating earnings of operators while decreasing the cost of MDs. Considering the diverse computation demands in different regions and variational computing Resources of edge servers, it is hard to solve this problem by traditional methods. To address this issue, an effective solution is generating an optimal computing Resource Configuration strategy and task offloading profile in time-varying UDN scenarios. Toward this end, a deep Q-network based scheme is proposed to achieve maximum long-term weighted network utility in such ever-changing environments. Simulation results validate the significant performance improvement of our scheme in weighted network utility and task offloading compared to conventional game-theoretical solution.
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Joint Computation Offloading and Resource Configuration in Ultra-Dense Edge Computing Networks: A Deep Reinforcement Learning Solution
2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019Co-Authors: Jianfeng Lv, Jingyu XiongAbstract:The prompt development of wireless communication network and emerging technologies such as Internet of Things (IoT) and 5G have increased the number of various mobile devices (MDs). In order to enlarge the capacity of the system and meet the high computation demands of MDs, the integration of ultra-dense heterogeneous networks (UDN) and mobile edge computing (MEC) is proposed as a promising paradigm. However, when massively deploying edge servers in UDN scenario, the operating expense reduction has become an essential issue to be solved, which can be achieved by computation offloading decision-making optimization and edge servers' computing Resource Configuration. In consideration of the complicated state information and ever-changing environment in UDN, applying reinforcement learning (RL) to the dynamical systems is envisioned as an effective way. Toward this end, we combine the deep learning with RL and propose a deep Qnetwork based method to address this high-dimensional problem. The experimental results demonstrate the superior performance of our proposed scheme on reducing the processing delay and enhancing the computing Resource utilization.
Jingyu Xiong - One of the best experts on this subject based on the ideXlab platform.
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Joint Computation Offloading and Resource Configuration in Ultra-Dense Edge Computing Networks: A Deep Reinforcement Learning Solution
2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019Co-Authors: Jianfeng Lv, Jingyu XiongAbstract:The prompt development of wireless communication network and emerging technologies such as Internet of Things (IoT) and 5G have increased the number of various mobile devices (MDs). In order to enlarge the capacity of the system and meet the high computation demands of MDs, the integration of ultra-dense heterogeneous networks (UDN) and mobile edge computing (MEC) is proposed as a promising paradigm. However, when massively deploying edge servers in UDN scenario, the operating expense reduction has become an essential issue to be solved, which can be achieved by computation offloading decision-making optimization and edge servers' computing Resource Configuration. In consideration of the complicated state information and ever-changing environment in UDN, applying reinforcement learning (RL) to the dynamical systems is envisioned as an effective way. Toward this end, we combine the deep learning with RL and propose a deep Qnetwork based method to address this high-dimensional problem. The experimental results demonstrate the superior performance of our proposed scheme on reducing the processing delay and enhancing the computing Resource utilization.
Chenyang Yang - One of the best experts on this subject based on the ideXlab platform.
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Joint Uplink and Downlink Resource Configuration for Ultra-reliable and Low-latency Communications
IEEE Transactions on Communications, 2018Co-Authors: Changyang She, Chenyang Yang, Tony Q. S. QuekAbstract:Supporting ultra-reliable and low-latency communications (URLLC) is one of the major goals for the fifth-generation cellular networks. Since spectrum usage efficiency is always a concern, and large bandwidth is required for ensuring stringent quality-of-service (QoS), we minimize the total bandwidth under the QoS constraints of URLLC. We first propose a packet delivery mechanism for URLLC. To reduce the required bandwidth for ensuring queueing delay, we consider a statistical multiplexing queueing mode, where the packets to be sent to different devices are waiting in one queue at the base station, and broadcast mode is adopted in downlink transmission. In this way, downlink bandwidth is shared among packets of multiple devices. In uplink transmission, different subchannels are allocated to different devices to avoid strong interference. Then, we jointly optimize uplink and downlink bandwidth Configuration and delay components to minimize the total bandwidth required to guarantee the overall packet loss and end-to-end delay, which includes uplink and downlink transmission delays, queueing delay and backhaul delay. We propose a two-step method to find the optimal solution. Simulation and numerical results validate our analysis and show remarkable performance gain by jointly optimizing uplink and downlink Configuration.
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Joint Uplink and Downlink Resource Configuration for Ultra-Reliable and Low-Latency Communications
IEEE Transactions on Communications, 2018Co-Authors: Chenyang Yang, Tony Q. S. QuekAbstract:Supporting ultra-reliable and low-latency communications (URLLC) is one of the major goals for the fifth-generation cellular networks. Since spectrum usage efficiency is always a concern, and large bandwidth is required for ensuring stringent quality-of-service (QoS), we minimize the total bandwidth under the QoS constraints of URLLC. We first propose a packet delivery mechanism for URLLC. To reduce the required bandwidth for ensuring queueing delay, we consider a statistical multiplexing queueing mode, where the packets to be sent to different devices are waiting in one queue at the base station, and broadcast mode is adopted in downlink transmission. In this way, downlink bandwidth is shared among packets of multiple devices. In uplink transmission, orthogonal subchannels are allocated to different devices to avoid strong interference. Then, we jointly optimize uplink and downlink bandwidth Configuration and delay components to minimize the total bandwidth required to guarantee the overall packet loss and end-to-end delay, which includes uplink and downlink transmission delays, queueing delay, and backhaul delay. We propose a two-step method to find the optimal solution. Simulation and numerical results validate our analysis and show remarkable performance gain by jointly optimizing uplink and downlink Configuration.
Odd Jarl Borch - One of the best experts on this subject based on the ideXlab platform.
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The Offshore Oil and Gas Operations in Ice Infested Water: Resource Configuration and Operational Process Management
Sustainable Shipping in a Changing Arctic, 2018Co-Authors: Odd Jarl Borch, Norvald KjerstadAbstract:In this chapter, we emphasize the fleet Configuration challenges of Arctic offshore oil and gas exploration. We highlight the role of offshore service vessels in achieving effective and safe oil and gas exploration activity in Arctic waters. We elaborate on the fleet Resource Configuration and operational management challenges. Data from case studies of operations in two High Arctic regions, the Disco Bay, Western Greenland and the Kara Sea in northwest Russia are revealed. The results show that the context of ice-infested waters, lack of infrastructure and risk related to weather and cold climate demands a more in-depth planning process including more companies and institutions, a more complex Resource Configuration with multi-functional vessels, and advanced Polar water competence as to logistics, managerial capacities, ice management and emergency preparedness. Implications for the industry and for further research are discussed.
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Resource Configuration and creative practices of community entrepreneurs
Journal of Enterprising Communities: People and Places in The Global Economy, 2008Co-Authors: Odd Jarl Borch, Anniken Førde, Lars Rønning, Ingebjørg Vestrum, Gry Agnete AlsosAbstract:Purpose – This paper aims to focus on the role of the community entrepreneur and the process of community entrepreneurship. It seeks to emphasize the social context as critical for gaining access to the Resources needed by a community venture and elaborates on the action pattern of the community entrepreneur towards raising critical Resources from the environment.Design/methodology/approach – The analysis is based on a longitudinal field study of community entrepreneurs in four Norwegian rural municipalities. The data consists of interviews, observations, and documents.Findings – Community entrepreneurs create local arenas and thereby facilitate cooperative entrepreneurial action, through bridging social capital. The actors are part of these community contexts and are involved in a range of reciprocal relations. Thus, the actors' creative practices toward the community have to run parallel with the Resource Configuration process.Research limitations/implications – Future studies may provide a broader empi...
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Resource Configuration competitive strategies and corporate entrepreneurship an empirical examination of small firms
Entrepreneurship Theory and Practice, 1999Co-Authors: Odd Jarl Borch, Morten Huse, Knut SennesethAbstract:Relationships between firm Resources and strategic orientations in small firms were explored in a study of 660 small firms. By using a Resource-based view of the firm, we considered entrepreneurshi...
