The Experts below are selected from a list of 21393 Experts worldwide ranked by ideXlab platform
Sanglu Lu - One of the best experts on this subject based on the ideXlab platform.
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INFOCOM - Quantized conflict graphs for wireless Network Optimization
2015 IEEE Conference on Computer Communications (INFOCOM), 2015Co-Authors: Yanchao Zhao, Wenzhong Li, Jie Wu, Sanglu LuAbstract:Conflict graph has been widely used for wireless Network Optimization in dealing with the issues of channel assignment, spectrum allocation, links scheduling and etc. Despite its simplicity, the traditional conflict graph suffers from two drawbacks. On one hand, it is a rough representation of the interference condition, which is inaccurate and will cause suboptimal results for wireless Network Optimization. On the other hand, it only defines the interference between two entities, which neglects the accumulative effect of small amount interference. In this paper, we propose the model of quantized conflict graph (QCG) to tackle the above issues. The properties, usage and construction methods of QCG are explored. We show that in its matrix form, a QCG owns the properties of low-rank and high-similarity. These properties give birth to three complementary QCG estimation strategies, namely low-rank approximation approach, similarity based approach, and comprehensive approach, to construct the QCG efficiently and accurately from partial interference measurement results. We further explore the potential of QCG for wireless Network Optimization by applying QCG in minimizing the total Network interference. Extensive experiments using real collected wireless Network are conducted to evaluate the system performance, which confirm the efficiency of the proposed algorithms.
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Quantized conflict graphs for wireless Network Optimization
Proceedings - IEEE INFOCOM, 2015Co-Authors: Yanchao Zhao, Wenzhong Li, Jie Wu, Sanglu LuAbstract:Conflict graph has been widely used for wireless Network Optimization in dealing with the issues of channel assignment, spectrum allocation, links scheduling and etc. Despite its simplicity, the traditional conflict graph suffers from two drawbacks. On one hand, it is a rough representation of the interference condition, which is inaccurate and will cause suboptimal results for wireless Network Optimization. On the other hand, it only defines the interference between two entities, which neglects the accumulative effect of small amount interference. In this paper, we propose the model of quantized conflict graph (QCG) to tackle the above issues. The properties, usage and construction methods of QCG are explored. We show that in its matrix form, a QCG owns the properties of low-rank and high-similarity. These properties give birth to three complementary QCG estimation strategies, namely low-rank approximation approach, similarity based approach, and comprehensive approach, to construct the QCG efficiently and accurately from partial interference measurement results. We further explore the potential of QCG for wireless Network Optimization by applying QCG in minimizing the total Network interference. Extensive experiments using real collected wireless Network are conducted to evaluate the system performance, which confirm the efficiency of the proposed algorithms.
Yanchao Zhao - One of the best experts on this subject based on the ideXlab platform.
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INFOCOM - Quantized conflict graphs for wireless Network Optimization
2015 IEEE Conference on Computer Communications (INFOCOM), 2015Co-Authors: Yanchao Zhao, Wenzhong Li, Jie Wu, Sanglu LuAbstract:Conflict graph has been widely used for wireless Network Optimization in dealing with the issues of channel assignment, spectrum allocation, links scheduling and etc. Despite its simplicity, the traditional conflict graph suffers from two drawbacks. On one hand, it is a rough representation of the interference condition, which is inaccurate and will cause suboptimal results for wireless Network Optimization. On the other hand, it only defines the interference between two entities, which neglects the accumulative effect of small amount interference. In this paper, we propose the model of quantized conflict graph (QCG) to tackle the above issues. The properties, usage and construction methods of QCG are explored. We show that in its matrix form, a QCG owns the properties of low-rank and high-similarity. These properties give birth to three complementary QCG estimation strategies, namely low-rank approximation approach, similarity based approach, and comprehensive approach, to construct the QCG efficiently and accurately from partial interference measurement results. We further explore the potential of QCG for wireless Network Optimization by applying QCG in minimizing the total Network interference. Extensive experiments using real collected wireless Network are conducted to evaluate the system performance, which confirm the efficiency of the proposed algorithms.
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Quantized conflict graphs for wireless Network Optimization
Proceedings - IEEE INFOCOM, 2015Co-Authors: Yanchao Zhao, Wenzhong Li, Jie Wu, Sanglu LuAbstract:Conflict graph has been widely used for wireless Network Optimization in dealing with the issues of channel assignment, spectrum allocation, links scheduling and etc. Despite its simplicity, the traditional conflict graph suffers from two drawbacks. On one hand, it is a rough representation of the interference condition, which is inaccurate and will cause suboptimal results for wireless Network Optimization. On the other hand, it only defines the interference between two entities, which neglects the accumulative effect of small amount interference. In this paper, we propose the model of quantized conflict graph (QCG) to tackle the above issues. The properties, usage and construction methods of QCG are explored. We show that in its matrix form, a QCG owns the properties of low-rank and high-similarity. These properties give birth to three complementary QCG estimation strategies, namely low-rank approximation approach, similarity based approach, and comprehensive approach, to construct the QCG efficiently and accurately from partial interference measurement results. We further explore the potential of QCG for wireless Network Optimization by applying QCG in minimizing the total Network interference. Extensive experiments using real collected wireless Network are conducted to evaluate the system performance, which confirm the efficiency of the proposed algorithms.
Jie Wu - One of the best experts on this subject based on the ideXlab platform.
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INFOCOM - Quantized conflict graphs for wireless Network Optimization
2015 IEEE Conference on Computer Communications (INFOCOM), 2015Co-Authors: Yanchao Zhao, Wenzhong Li, Jie Wu, Sanglu LuAbstract:Conflict graph has been widely used for wireless Network Optimization in dealing with the issues of channel assignment, spectrum allocation, links scheduling and etc. Despite its simplicity, the traditional conflict graph suffers from two drawbacks. On one hand, it is a rough representation of the interference condition, which is inaccurate and will cause suboptimal results for wireless Network Optimization. On the other hand, it only defines the interference between two entities, which neglects the accumulative effect of small amount interference. In this paper, we propose the model of quantized conflict graph (QCG) to tackle the above issues. The properties, usage and construction methods of QCG are explored. We show that in its matrix form, a QCG owns the properties of low-rank and high-similarity. These properties give birth to three complementary QCG estimation strategies, namely low-rank approximation approach, similarity based approach, and comprehensive approach, to construct the QCG efficiently and accurately from partial interference measurement results. We further explore the potential of QCG for wireless Network Optimization by applying QCG in minimizing the total Network interference. Extensive experiments using real collected wireless Network are conducted to evaluate the system performance, which confirm the efficiency of the proposed algorithms.
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Quantized conflict graphs for wireless Network Optimization
Proceedings - IEEE INFOCOM, 2015Co-Authors: Yanchao Zhao, Wenzhong Li, Jie Wu, Sanglu LuAbstract:Conflict graph has been widely used for wireless Network Optimization in dealing with the issues of channel assignment, spectrum allocation, links scheduling and etc. Despite its simplicity, the traditional conflict graph suffers from two drawbacks. On one hand, it is a rough representation of the interference condition, which is inaccurate and will cause suboptimal results for wireless Network Optimization. On the other hand, it only defines the interference between two entities, which neglects the accumulative effect of small amount interference. In this paper, we propose the model of quantized conflict graph (QCG) to tackle the above issues. The properties, usage and construction methods of QCG are explored. We show that in its matrix form, a QCG owns the properties of low-rank and high-similarity. These properties give birth to three complementary QCG estimation strategies, namely low-rank approximation approach, similarity based approach, and comprehensive approach, to construct the QCG efficiently and accurately from partial interference measurement results. We further explore the potential of QCG for wireless Network Optimization by applying QCG in minimizing the total Network interference. Extensive experiments using real collected wireless Network are conducted to evaluate the system performance, which confirm the efficiency of the proposed algorithms.
Wenzhong Li - One of the best experts on this subject based on the ideXlab platform.
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INFOCOM - Quantized conflict graphs for wireless Network Optimization
2015 IEEE Conference on Computer Communications (INFOCOM), 2015Co-Authors: Yanchao Zhao, Wenzhong Li, Jie Wu, Sanglu LuAbstract:Conflict graph has been widely used for wireless Network Optimization in dealing with the issues of channel assignment, spectrum allocation, links scheduling and etc. Despite its simplicity, the traditional conflict graph suffers from two drawbacks. On one hand, it is a rough representation of the interference condition, which is inaccurate and will cause suboptimal results for wireless Network Optimization. On the other hand, it only defines the interference between two entities, which neglects the accumulative effect of small amount interference. In this paper, we propose the model of quantized conflict graph (QCG) to tackle the above issues. The properties, usage and construction methods of QCG are explored. We show that in its matrix form, a QCG owns the properties of low-rank and high-similarity. These properties give birth to three complementary QCG estimation strategies, namely low-rank approximation approach, similarity based approach, and comprehensive approach, to construct the QCG efficiently and accurately from partial interference measurement results. We further explore the potential of QCG for wireless Network Optimization by applying QCG in minimizing the total Network interference. Extensive experiments using real collected wireless Network are conducted to evaluate the system performance, which confirm the efficiency of the proposed algorithms.
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Quantized conflict graphs for wireless Network Optimization
Proceedings - IEEE INFOCOM, 2015Co-Authors: Yanchao Zhao, Wenzhong Li, Jie Wu, Sanglu LuAbstract:Conflict graph has been widely used for wireless Network Optimization in dealing with the issues of channel assignment, spectrum allocation, links scheduling and etc. Despite its simplicity, the traditional conflict graph suffers from two drawbacks. On one hand, it is a rough representation of the interference condition, which is inaccurate and will cause suboptimal results for wireless Network Optimization. On the other hand, it only defines the interference between two entities, which neglects the accumulative effect of small amount interference. In this paper, we propose the model of quantized conflict graph (QCG) to tackle the above issues. The properties, usage and construction methods of QCG are explored. We show that in its matrix form, a QCG owns the properties of low-rank and high-similarity. These properties give birth to three complementary QCG estimation strategies, namely low-rank approximation approach, similarity based approach, and comprehensive approach, to construct the QCG efficiently and accurately from partial interference measurement results. We further explore the potential of QCG for wireless Network Optimization by applying QCG in minimizing the total Network interference. Extensive experiments using real collected wireless Network are conducted to evaluate the system performance, which confirm the efficiency of the proposed algorithms.
Elizabeth S. Bentley - One of the best experts on this subject based on the ideXlab platform.
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Hybrid-Beamforming-Based Millimeter-Wave Cellular Network Optimization
IEEE Journal on Selected Areas in Communications, 2019Co-Authors: Elizabeth S. BentleyAbstract:Massive MIMO and millimeter-wave communication (mmWave) have recently emerged as two key technologies for building 5G wireless Networks and beyond. To reconcile the conflict between the large antenna arrays and the limited amount of radio-frequency (RF) chains in mmWave systems, the so-called hybrid beamforming becomes a promising solution and has received a great deal of attention in recent years. However, existing research on hybrid beamforming focused mostly on the physical layer or signal processing aspects. So far, there is a lack of theoretical understanding of how hybrid beamforming could affect mmWave Network Optimization. In this paper, we consider the impacts of hybrid beamforming on utility-optimality and queuing delay in mmWave cellular Network Optimization. Our contributions in this paper are three-fold: i) we develop a joint hybrid beamforming and congestion control algorithmic framework for mmWave Network utility maximization; ii) we reveal a pseudoconvexity structure in the hybrid beamforming scheduling problem, which leads to simplified analog beamforming protocol design; and iii) we theoretically characterize the scalings of utility-optimality and delay with respect to channel state information (CSI) accuracy in digital beamforming.
