The Experts below are selected from a list of 147321 Experts worldwide ranked by ideXlab platform
Chonggang Wang - One of the best experts on this subject based on the ideXlab platform.
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recent advances in cloud radio access networks System Architectures key techniques and open issues
IEEE Communications Surveys and Tutorials, 2016Co-Authors: Mugen Peng, Yaohua Sun, Zhendong Mao, Chonggang WangAbstract:As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including System Architectures, key techniques, and open issues. The System Architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well.
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Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
IEEE Communications Surveys and Tutorials, 2016Co-Authors: Mugen Peng, Zhengdong Mao, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang WangAbstract:As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including System Architectures, key techniques, and open issues. The System Architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, socialaware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.
Alberto Sangiovannivincentelli - One of the best experts on this subject based on the ideXlab platform.
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optimized selection of reliable and cost effective safety critical System Architectures
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020Co-Authors: Pierluigi Nuzzo, Nikunj Bajaj, Michael Masin, Dmitrii Kirov, Roberto Passerone, Alberto SangiovannivincentelliAbstract:We address the problem of synthesizing safety-critical embedded and cyber-physical System Architectures to minimize a cost function while guaranteeing the desired reliability. We represent a System architecture as a configurable graph in which both the nodes (components) and edges (interconnections) may fail. We then propose a compact analytical formalism to efficiently reason about the reliability of the overall System based on the failure probabilities of the components, and provide expressions of the design constraints that avoid exhaustive enumeration of failure cases on all possible graph configurations. Based on these constraints, we cast the synthesis problem as an optimization problem and propose monolithic and iterative optimization schemes to decrease the problem complexity. We implement the proposed algorithms in the ArchEx framework, leveraging a pattern-based specification language to facilitate problem formulation. Design problems from aircraft electric power distribution networks and reconfigurable industrial manufacturing Systems illustrate the effectiveness of our approach.
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archex an extensible framework for the exploration of cyber physical System Architectures
Design Automation Conference, 2017Co-Authors: Dmitrii Kirov, Pierluigi Nuzzo, Roberto Passerone, Alberto SangiovannivincentelliAbstract:We present ArchEx, a framework for cyber-physical System architecture exploration. We formulate the exploration problem as a mapping problem, where "virtual" components are mapped into "real" components from pre-defined libraries to minimize an objective function while guaranteeing that System requirements are satisfied. ArchEx leverages an extensible set of patterns to enable formal, yet flexible, requirement specification, a graph-based internal representation of the System architecture, and algorithms based on mixed integer linear programming to solve the mapping problem. Its effectiveness is demonstrated on two industrial case studies: an aircraft power distribution network and a reconfigurable automated production line.
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optimized selection of reliable and cost effective cyber physical System Architectures
Design Automation and Test in Europe, 2015Co-Authors: Nikunj Bajaj, Pierluigi Nuzzo, Michael Masin, Alberto SangiovannivincentelliAbstract:We address the problem of synthesizing safety-critical cyber-physical System Architectures to minimize a cost function while guaranteeing the desired reliability. We cast the problem as an integer linear program on a reconfigurable graph which models the architecture. Since generating symbolic probability constraints by exhaustive enumeration of failure cases on all possible graph configurations takes exponential time, we propose two algorithms to decrease the problem complexity, i.e. Integer-Linear Programming Modulo Reliability (ILP-MR) and Integer-Linear Programming with Approximate Reliability (ILP-AR). We compare the two approaches and demonstrate their effectiveness on the design of aircraft electric power System Architectures.
Mugen Peng - One of the best experts on this subject based on the ideXlab platform.
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recent advances in cloud radio access networks System Architectures key techniques and open issues
IEEE Communications Surveys and Tutorials, 2016Co-Authors: Mugen Peng, Yaohua Sun, Zhendong Mao, Chonggang WangAbstract:As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including System Architectures, key techniques, and open issues. The System Architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well.
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Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
IEEE Communications Surveys and Tutorials, 2016Co-Authors: Mugen Peng, Zhengdong Mao, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang WangAbstract:As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including System Architectures, key techniques, and open issues. The System Architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, socialaware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.
Yaohua Sun - One of the best experts on this subject based on the ideXlab platform.
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recent advances in cloud radio access networks System Architectures key techniques and open issues
IEEE Communications Surveys and Tutorials, 2016Co-Authors: Mugen Peng, Yaohua Sun, Zhendong Mao, Chonggang WangAbstract:As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including System Architectures, key techniques, and open issues. The System Architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well.
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Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
IEEE Communications Surveys and Tutorials, 2016Co-Authors: Mugen Peng, Zhengdong Mao, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang WangAbstract:As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including System Architectures, key techniques, and open issues. The System Architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, socialaware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.
Zhendong Mao - One of the best experts on this subject based on the ideXlab platform.
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recent advances in cloud radio access networks System Architectures key techniques and open issues
IEEE Communications Surveys and Tutorials, 2016Co-Authors: Mugen Peng, Yaohua Sun, Zhendong Mao, Chonggang WangAbstract:As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including System Architectures, key techniques, and open issues. The System Architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well.
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Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
IEEE Communications Surveys and Tutorials, 2016Co-Authors: Mugen Peng, Zhengdong Mao, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang WangAbstract:As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including System Architectures, key techniques, and open issues. The System Architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, socialaware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.
