The Experts below are selected from a list of 51504 Experts worldwide ranked by ideXlab platform

Chonggang Wang - One of the best experts on this subject based on the ideXlab platform.

  • recent advances in cloud Radio access networks system architectures key techniques and open issues
    IEEE Communications Surveys and Tutorials, 2016
    Co-Authors: Mugen Peng, Yaohua Sun, Zhendong Mao, Chonggang Wang
    Abstract:

    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.

  • Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
    IEEE Communications Surveys and Tutorials, 2016
    Co-Authors: Mugen Peng, Zhengdong Mao, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang Wang
    Abstract:

    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.

Mugen Peng - One of the best experts on this subject based on the ideXlab platform.

  • recent advances in cloud Radio access networks system architectures key techniques and open issues
    IEEE Communications Surveys and Tutorials, 2016
    Co-Authors: Mugen Peng, Yaohua Sun, Zhendong Mao, Chonggang Wang
    Abstract:

    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.

  • Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
    IEEE Communications Surveys and Tutorials, 2016
    Co-Authors: Mugen Peng, Zhengdong Mao, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang Wang
    Abstract:

    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.

Hungyun Hsieh - One of the best experts on this subject based on the ideXlab platform.

  • an investigation of primary transmitter detection techniques in cognitive Radio networks from network optimization perspective
    Wireless Communications and Networking Conference, 2008
    Co-Authors: Hungyun Hsieh
    Abstract:

    In this paper, we investigate the problem of primary user detection in cognitive Radio networks. Compared with related work that aims to propose techniques at different layers of the network protocol stack for detecting primary users, we aim to investigate the capabilities and limitations of different primary user detection techniques from the perspective of network optimization. The goal is to understand fundamental performance tradeoffs of these techniques without being limited by existing cognitive Radio Software and hardware platforms. To proceed, we first identify several dimensions for designing primary transmitter detection techniques in cognitive Radio networks, including transmitter side vs. receiver side detection, and collaborative vs. non-collaborative detection. We then formulate primary transmitter detection techniques along these dimensions using mixed-integer nonlinear programming (MINLP). Evaluation results show the benefits of using the proposed optimization framework to profile the fundamental characteristics of primary transmitter detection techniques, thus motivating future research along this direction.

  • WCNC - An Investigation of Primary Transmitter Detection Techniques in Cognitive Radio Networks from Network Optimization Perspective
    2008 IEEE Wireless Communications and Networking Conference, 2008
    Co-Authors: Hungyun Hsieh
    Abstract:

    In this paper, we investigate the problem of primary user detection in cognitive Radio networks. Compared with related work that aims to propose techniques at different layers of the network protocol stack for detecting primary users, we aim to investigate the capabilities and limitations of different primary user detection techniques from the perspective of network optimization. The goal is to understand fundamental performance tradeoffs of these techniques without being limited by existing cognitive Radio Software and hardware platforms. To proceed, we first identify several dimensions for designing primary transmitter detection techniques in cognitive Radio networks, including transmitter side vs. receiver side detection, and collaborative vs. non-collaborative detection. We then formulate primary transmitter detection techniques along these dimensions using mixed-integer nonlinear programming (MINLP). Evaluation results show the benefits of using the proposed optimization framework to profile the fundamental characteristics of primary transmitter detection techniques, thus motivating future research along this direction.

  • GLOBECOM - Modeling and Comparison of Primary User Detection Techniques in Cognitive Radio Networks
    IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference, 2008
    Co-Authors: You-en Lin, Hungyun Hsieh
    Abstract:

    In this paper, we investigate the problem of spectrum sensing in cognitive Radio networks. Compared with related work that aims to propose techniques at different layers of the network protocol stack for detecting primary users, we aim to investigate the capabilities and limitations of different primary user detection techniques from the perspective of network optimization. The goal is to understand the fundamental performance tradeoffs of different primary user detection techniques without being limited by existing cognitive Radio Software and hardware platforms. To proceed, we first identify several dimensions for designing primary user detection techniques in cognitive Radio networks, and then formulate primary user detection techniques using mixed- integer nonlinear programming (MINLP). Evaluation results show the benefits of using the proposed optimization framework for profiling the fundamental characteristics of primary user detection techniques.

Zhendong Mao - One of the best experts on this subject based on the ideXlab platform.

  • recent advances in cloud Radio access networks system architectures key techniques and open issues
    IEEE Communications Surveys and Tutorials, 2016
    Co-Authors: Mugen Peng, Yaohua Sun, Zhendong Mao, Chonggang Wang
    Abstract:

    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.

  • Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
    IEEE Communications Surveys and Tutorials, 2016
    Co-Authors: Mugen Peng, Zhengdong Mao, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang Wang
    Abstract:

    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.

  • recent advances in cloud Radio access networks system architectures key techniques and open issues
    IEEE Communications Surveys and Tutorials, 2016
    Co-Authors: Mugen Peng, Yaohua Sun, Zhendong Mao, Chonggang Wang
    Abstract:

    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.

  • Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
    IEEE Communications Surveys and Tutorials, 2016
    Co-Authors: Mugen Peng, Zhengdong Mao, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang Wang
    Abstract:

    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.