The Experts below are selected from a list of 59526 Experts worldwide ranked by ideXlab platform
Gang Feng - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Game theoretical bandwidth request Allocation Strategy in P2P streaming systems
2013 IEEE Global Communications Conference (GLOBECOM), 2013Co-Authors: Jiang Zhou, Zhuo Chen, Gang FengAbstract:Due to the merits of lower bandwidth consumption at streaming server and higher scalability, P2P streaming systems have been widely developed and deployed. However, the heterogeneity of bandwidth resource and playback position at peers may easily lead to load unbalancing problem, especially in the era of emerging booming if mobile Internet applications. This may severely deteriorate video playback quality at peers. In this paper we study bandwidth request Allocation Strategy, aiming at balancing the traffic load at peers and thus improving peers' playback quality in P2P streaming networks. We formulate a non-cooperative game model to analysis the bandwidth resource competition between multiple requesting peers and service peers, through searching the Nash Equilibrium of this game, the optimal bandwidth requesting Strategy can be obtained. Then an distributed algorithm is proposed, called Game based Bandwidth Request Allocation Strategy (GBRA). We conduct simulation experiments to validate the effectiveness of GBRA, and numerical results show that the proposed Strategy can significantly improve the load unbalancing problem in P2P streaming system and decrease the latency of streaming data retrieval at peers in P2P streaming networks, compared with the classical bandwidth request Allocation strategies: proportional Strategy and greedy Strategy.
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Game theoretical bandwidth request Allocation Strategy in P2P streaming systems
2013 IEEE Global Communications Conference (GLOBECOM), 2013Co-Authors: Jiang Zhou, Zhuo Chen, Gang FengAbstract:Due to the merits of lower bandwidth consumption at streaming server and higher scalability, P2P streaming systems have been widely developed and deployed. However, the heterogeneity of bandwidth resource and playback position at peers may easily lead to load unbalancing problem, especially in the era of emerging booming if mobile Internet applications. This may severely deteriorate video playback quality at peers. In this paper we study bandwidth request Allocation Strategy, aiming at balancing the traffic load at peers and thus improving peers' playback quality in P2P streaming networks. We formulate a non-cooperative game model to analysis the bandwidth resource competition between multiple requesting peers and service peers, through searching the Nash Equilibrium of this game, the optimal bandwidth requesting Strategy can be obtained. Then an distributed algorithm is proposed, called Game based Bandwidth Request Allocation Strategy (GBRA). We conduct simulation experiments to validate the effectiveness of GBRA, and numerical results show that the proposed Strategy can significantly improve the load unbalancing problem in P2P streaming system and decrease the latency of streaming data retrieval at peers in P2P streaming networks, compared with the classical bandwidth request Allocation strategies: proportional Strategy and greedy Strategy.
Jiang Zhou - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Game theoretical bandwidth request Allocation Strategy in P2P streaming systems
2013 IEEE Global Communications Conference (GLOBECOM), 2013Co-Authors: Jiang Zhou, Zhuo Chen, Gang FengAbstract:Due to the merits of lower bandwidth consumption at streaming server and higher scalability, P2P streaming systems have been widely developed and deployed. However, the heterogeneity of bandwidth resource and playback position at peers may easily lead to load unbalancing problem, especially in the era of emerging booming if mobile Internet applications. This may severely deteriorate video playback quality at peers. In this paper we study bandwidth request Allocation Strategy, aiming at balancing the traffic load at peers and thus improving peers' playback quality in P2P streaming networks. We formulate a non-cooperative game model to analysis the bandwidth resource competition between multiple requesting peers and service peers, through searching the Nash Equilibrium of this game, the optimal bandwidth requesting Strategy can be obtained. Then an distributed algorithm is proposed, called Game based Bandwidth Request Allocation Strategy (GBRA). We conduct simulation experiments to validate the effectiveness of GBRA, and numerical results show that the proposed Strategy can significantly improve the load unbalancing problem in P2P streaming system and decrease the latency of streaming data retrieval at peers in P2P streaming networks, compared with the classical bandwidth request Allocation strategies: proportional Strategy and greedy Strategy.
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Game theoretical bandwidth request Allocation Strategy in P2P streaming systems
2013 IEEE Global Communications Conference (GLOBECOM), 2013Co-Authors: Jiang Zhou, Zhuo Chen, Gang FengAbstract:Due to the merits of lower bandwidth consumption at streaming server and higher scalability, P2P streaming systems have been widely developed and deployed. However, the heterogeneity of bandwidth resource and playback position at peers may easily lead to load unbalancing problem, especially in the era of emerging booming if mobile Internet applications. This may severely deteriorate video playback quality at peers. In this paper we study bandwidth request Allocation Strategy, aiming at balancing the traffic load at peers and thus improving peers' playback quality in P2P streaming networks. We formulate a non-cooperative game model to analysis the bandwidth resource competition between multiple requesting peers and service peers, through searching the Nash Equilibrium of this game, the optimal bandwidth requesting Strategy can be obtained. Then an distributed algorithm is proposed, called Game based Bandwidth Request Allocation Strategy (GBRA). We conduct simulation experiments to validate the effectiveness of GBRA, and numerical results show that the proposed Strategy can significantly improve the load unbalancing problem in P2P streaming system and decrease the latency of streaming data retrieval at peers in P2P streaming networks, compared with the classical bandwidth request Allocation strategies: proportional Strategy and greedy Strategy.
Zhuo Chen - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Game theoretical bandwidth request Allocation Strategy in P2P streaming systems
2013 IEEE Global Communications Conference (GLOBECOM), 2013Co-Authors: Jiang Zhou, Zhuo Chen, Gang FengAbstract:Due to the merits of lower bandwidth consumption at streaming server and higher scalability, P2P streaming systems have been widely developed and deployed. However, the heterogeneity of bandwidth resource and playback position at peers may easily lead to load unbalancing problem, especially in the era of emerging booming if mobile Internet applications. This may severely deteriorate video playback quality at peers. In this paper we study bandwidth request Allocation Strategy, aiming at balancing the traffic load at peers and thus improving peers' playback quality in P2P streaming networks. We formulate a non-cooperative game model to analysis the bandwidth resource competition between multiple requesting peers and service peers, through searching the Nash Equilibrium of this game, the optimal bandwidth requesting Strategy can be obtained. Then an distributed algorithm is proposed, called Game based Bandwidth Request Allocation Strategy (GBRA). We conduct simulation experiments to validate the effectiveness of GBRA, and numerical results show that the proposed Strategy can significantly improve the load unbalancing problem in P2P streaming system and decrease the latency of streaming data retrieval at peers in P2P streaming networks, compared with the classical bandwidth request Allocation strategies: proportional Strategy and greedy Strategy.
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Game theoretical bandwidth request Allocation Strategy in P2P streaming systems
2013 IEEE Global Communications Conference (GLOBECOM), 2013Co-Authors: Jiang Zhou, Zhuo Chen, Gang FengAbstract:Due to the merits of lower bandwidth consumption at streaming server and higher scalability, P2P streaming systems have been widely developed and deployed. However, the heterogeneity of bandwidth resource and playback position at peers may easily lead to load unbalancing problem, especially in the era of emerging booming if mobile Internet applications. This may severely deteriorate video playback quality at peers. In this paper we study bandwidth request Allocation Strategy, aiming at balancing the traffic load at peers and thus improving peers' playback quality in P2P streaming networks. We formulate a non-cooperative game model to analysis the bandwidth resource competition between multiple requesting peers and service peers, through searching the Nash Equilibrium of this game, the optimal bandwidth requesting Strategy can be obtained. Then an distributed algorithm is proposed, called Game based Bandwidth Request Allocation Strategy (GBRA). We conduct simulation experiments to validate the effectiveness of GBRA, and numerical results show that the proposed Strategy can significantly improve the load unbalancing problem in P2P streaming system and decrease the latency of streaming data retrieval at peers in P2P streaming networks, compared with the classical bandwidth request Allocation strategies: proportional Strategy and greedy Strategy.
Feng Guo-hu - One of the best experts on this subject based on the ideXlab platform.
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Control Allocation Strategy for Composite Control of Saucer-like Air Vehicle
Computer Simulation, 2008Co-Authors: Feng Guo-huAbstract:Two kinds of control Allocation strategies were proposed for composite control of saucer-like air vehicle.Nonlinear Allocation Strategy is based on output error;optimal Allocation is based on energy.With the two strategies,the regulation task is separated from the control distribution task.The control law specifies only the total control effort.The distribution of control is decided by a separate control Allocation module.Simulation shows that with the two designs moving mass and thrust vectoring control units complement each other and obtains preferable control results when control constrained isn't considered.Compared with nonlinear Allocation Strategy,optimal Allocation Strategy avoids the influence of control constrained.
Lingjiang Kong - One of the best experts on this subject based on the ideXlab platform.
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joint node selection and power Allocation Strategy for multitarget tracking in decentralized radar networks
IEEE Transactions on Signal Processing, 2018Co-Authors: Wei Yi, Thia Kirubarajan, Lingjiang KongAbstract:Networked radar systems have been demonstrated to offer enhanced target tracking capabilities. An effective radar resource Allocation Strategy can efficiently optimize system parameters, leading to performance enhancements. In this paper, two critical but limited system resources are considered for optimization: the number of radar nodes and the transmitted power. In this scenario, a joint node selection and power Allocation (JSPA) Strategy is developed with the objective of tracking multiple targets. The proposed mechanism implements the optimal resource Allocation based on the feedback information in the tracking recursion cycle in order to improve the worst-case tracking accuracy with multiple targets. The network architecture considered in this paper is decentralized so that communication requirements may be reduced while maintaining system robustness. Since the predicted conditional Cramer–Rao lower bound (PC-CRLB) provides a lower bound on the accuracy of the target state estimates conditional on the actual measurement realizations, it is more accurate than the standard posterior CRLB and is thus derived and used as an optimization criterion for the JSPA Strategy. It is shown that the optimal JSPA is a two-variable nonconvex optimization problem. We propose an efficient two-step semidefinite programming based solution to solve this problem. Numerical results demonstrate the superior performance of the proposed Strategy and the effectiveness of the proposed solution.
