The Experts below are selected from a list of 505302 Experts worldwide ranked by ideXlab platform
K M Wong - One of the best experts on this subject based on the ideXlab platform.
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worst case Performance Optimization for robust power control in downlink beamforming
Signal Processing, 2013Co-Authors: Tingting Liu, Jiankang Zhang, K M WongAbstract:Abstract In this paper, 1 we examine the problem of robust power control in a downlink beamforming environment under uncertain channel state information (CSI). We suggest that the method of power control using the lower bounds of signal-to-interference-and-noise ratio (SINR) is too pessimistic and will require significantly higher power in transmission than is necessary in practice. Here, a new robust downlink power control solution based on worst-case Performance Optimization is developed. Our approach employs the explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices and optimizes the amount of transmission power while guaranteeing the worst-case Performance to satisfy the quality of service (QoS) constraints for all users. This Optimization problem is non-convex and intractable. In order to arrive at an optimal solution to the problem, we propose an iterative algorithm to find the optimum power allocation and worst-case uncertainty matrices. The iterative algorithm is based on the efficient solving of the worst-case uncertainty matrices once the transmission power is given. This can be done by finding the solutions for two cases: (a) when the uncertainty on the DCC matrices is small, for which a closed-form optimum solution can be obtained and (b) when the uncertainty is substantial, for which the intractable problem is transformed into a convex Optimization problem readily solvable by an interior point method. Simulation results show that the proposed robust downlink power control using the approach of worst-case Performance Optimization converges in a few iterations and reduces the transmission power effectively under imperfect knowledge of the channel condition.
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robust downlink power control using worst case Performance Optimization
International Symposium on Information Theory, 2008Co-Authors: Tingting Liu, Jiankang Zhang, K M WongAbstract:In this paper, a new robust downlink power control solution based on worst-case Performance Optimization is developed. Our approach depends on explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices, worst-case Performance Optimization and guarantees that the quality of service (QoS) constraints are satisfied for all users using minimum amount of power. An iterative algorithm to find the optimum power allocation is proposed. The key to this algorithm is to solve an Optimization problem to obtain worst-case uncertainty matrices. When the uncertainty is small enough to guarantee that the DCC matrices are positive semidefinite, we obtain a closed-form solution of the subproblem. When the uncertainty is large, we transform this intractable problem into a convex problem. Simulation results show that our proposed robust downlink power control using the approach of worst-case Performance Optimization reduces the transmission power effectively under imperfect knowledge of the channel condition.
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efficient design for robust downlink power control using worst case Performance Optimization
Biennial Symposium on Communications, 2008Co-Authors: Tingting Liu, Jiankang Zhang, K M WongAbstract:In this paper, a new robust downlink power control solution based on worst-case Performance Optimization is developed. Our approach depends on explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices, worst-case Performance Optimization and guarantees that the quality of service (QoS) constraints are satisfied for all users using minimum amount of power. The key in the iteration is the step to solve an originally non-convex problem to obtain worst-case uncertainty matrices. When the uncertainty is small enough to guarantee that the DCC matrices are positive semidefinite, we obtain a closed-form solution of this problem. When the uncertainty is large, we transform this intractable problem into a convex problem. Simulation results show that our proposed robust downlink power control using the approach of worst-case Performance Optimization reduces the transmission power effectively under imperfect knowledge of the channel condition.
Tingting Liu - One of the best experts on this subject based on the ideXlab platform.
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worst case Performance Optimization for robust power control in downlink beamforming
Signal Processing, 2013Co-Authors: Tingting Liu, Jiankang Zhang, K M WongAbstract:Abstract In this paper, 1 we examine the problem of robust power control in a downlink beamforming environment under uncertain channel state information (CSI). We suggest that the method of power control using the lower bounds of signal-to-interference-and-noise ratio (SINR) is too pessimistic and will require significantly higher power in transmission than is necessary in practice. Here, a new robust downlink power control solution based on worst-case Performance Optimization is developed. Our approach employs the explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices and optimizes the amount of transmission power while guaranteeing the worst-case Performance to satisfy the quality of service (QoS) constraints for all users. This Optimization problem is non-convex and intractable. In order to arrive at an optimal solution to the problem, we propose an iterative algorithm to find the optimum power allocation and worst-case uncertainty matrices. The iterative algorithm is based on the efficient solving of the worst-case uncertainty matrices once the transmission power is given. This can be done by finding the solutions for two cases: (a) when the uncertainty on the DCC matrices is small, for which a closed-form optimum solution can be obtained and (b) when the uncertainty is substantial, for which the intractable problem is transformed into a convex Optimization problem readily solvable by an interior point method. Simulation results show that the proposed robust downlink power control using the approach of worst-case Performance Optimization converges in a few iterations and reduces the transmission power effectively under imperfect knowledge of the channel condition.
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robust downlink power control using worst case Performance Optimization
International Symposium on Information Theory, 2008Co-Authors: Tingting Liu, Jiankang Zhang, K M WongAbstract:In this paper, a new robust downlink power control solution based on worst-case Performance Optimization is developed. Our approach depends on explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices, worst-case Performance Optimization and guarantees that the quality of service (QoS) constraints are satisfied for all users using minimum amount of power. An iterative algorithm to find the optimum power allocation is proposed. The key to this algorithm is to solve an Optimization problem to obtain worst-case uncertainty matrices. When the uncertainty is small enough to guarantee that the DCC matrices are positive semidefinite, we obtain a closed-form solution of the subproblem. When the uncertainty is large, we transform this intractable problem into a convex problem. Simulation results show that our proposed robust downlink power control using the approach of worst-case Performance Optimization reduces the transmission power effectively under imperfect knowledge of the channel condition.
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efficient design for robust downlink power control using worst case Performance Optimization
Biennial Symposium on Communications, 2008Co-Authors: Tingting Liu, Jiankang Zhang, K M WongAbstract:In this paper, a new robust downlink power control solution based on worst-case Performance Optimization is developed. Our approach depends on explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices, worst-case Performance Optimization and guarantees that the quality of service (QoS) constraints are satisfied for all users using minimum amount of power. The key in the iteration is the step to solve an originally non-convex problem to obtain worst-case uncertainty matrices. When the uncertainty is small enough to guarantee that the DCC matrices are positive semidefinite, we obtain a closed-form solution of this problem. When the uncertainty is large, we transform this intractable problem into a convex problem. Simulation results show that our proposed robust downlink power control using the approach of worst-case Performance Optimization reduces the transmission power effectively under imperfect knowledge of the channel condition.
Jiankang Zhang - One of the best experts on this subject based on the ideXlab platform.
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worst case Performance Optimization for robust power control in downlink beamforming
Signal Processing, 2013Co-Authors: Tingting Liu, Jiankang Zhang, K M WongAbstract:Abstract In this paper, 1 we examine the problem of robust power control in a downlink beamforming environment under uncertain channel state information (CSI). We suggest that the method of power control using the lower bounds of signal-to-interference-and-noise ratio (SINR) is too pessimistic and will require significantly higher power in transmission than is necessary in practice. Here, a new robust downlink power control solution based on worst-case Performance Optimization is developed. Our approach employs the explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices and optimizes the amount of transmission power while guaranteeing the worst-case Performance to satisfy the quality of service (QoS) constraints for all users. This Optimization problem is non-convex and intractable. In order to arrive at an optimal solution to the problem, we propose an iterative algorithm to find the optimum power allocation and worst-case uncertainty matrices. The iterative algorithm is based on the efficient solving of the worst-case uncertainty matrices once the transmission power is given. This can be done by finding the solutions for two cases: (a) when the uncertainty on the DCC matrices is small, for which a closed-form optimum solution can be obtained and (b) when the uncertainty is substantial, for which the intractable problem is transformed into a convex Optimization problem readily solvable by an interior point method. Simulation results show that the proposed robust downlink power control using the approach of worst-case Performance Optimization converges in a few iterations and reduces the transmission power effectively under imperfect knowledge of the channel condition.
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robust downlink power control using worst case Performance Optimization
International Symposium on Information Theory, 2008Co-Authors: Tingting Liu, Jiankang Zhang, K M WongAbstract:In this paper, a new robust downlink power control solution based on worst-case Performance Optimization is developed. Our approach depends on explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices, worst-case Performance Optimization and guarantees that the quality of service (QoS) constraints are satisfied for all users using minimum amount of power. An iterative algorithm to find the optimum power allocation is proposed. The key to this algorithm is to solve an Optimization problem to obtain worst-case uncertainty matrices. When the uncertainty is small enough to guarantee that the DCC matrices are positive semidefinite, we obtain a closed-form solution of the subproblem. When the uncertainty is large, we transform this intractable problem into a convex problem. Simulation results show that our proposed robust downlink power control using the approach of worst-case Performance Optimization reduces the transmission power effectively under imperfect knowledge of the channel condition.
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efficient design for robust downlink power control using worst case Performance Optimization
Biennial Symposium on Communications, 2008Co-Authors: Tingting Liu, Jiankang Zhang, K M WongAbstract:In this paper, a new robust downlink power control solution based on worst-case Performance Optimization is developed. Our approach depends on explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices, worst-case Performance Optimization and guarantees that the quality of service (QoS) constraints are satisfied for all users using minimum amount of power. The key in the iteration is the step to solve an originally non-convex problem to obtain worst-case uncertainty matrices. When the uncertainty is small enough to guarantee that the DCC matrices are positive semidefinite, we obtain a closed-form solution of this problem. When the uncertainty is large, we transform this intractable problem into a convex problem. Simulation results show that our proposed robust downlink power control using the approach of worst-case Performance Optimization reduces the transmission power effectively under imperfect knowledge of the channel condition.
Sangarapillai Lambotharan - One of the best experts on this subject based on the ideXlab platform.
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Robust Multiuser Downlink Beamforming with Per Antenna Power Constraints using Worst-Case Performance Optimization
2008 International Symposium on Communications and Information Technologies, 2008Co-Authors: Vimal Sharma, Sangarapillai LambotharanAbstract:We propose a robust solution to the problem of multiuser downlink beamforming with constraints on per antenna power and quality of services (QoS). We assume only the erroneous channel state information (CSI) is available at the transmitter, which may arise due to quantization, feedback delay, feedback error, etc., and solve this problem within the framework of worst-case Performance Optimization. Simulations results confirm that the worst-case based robust solution outperforms the existing non-robust solutions and satisfies the QoS constraints with probability one while the non-robust scheme satisfies the QoS only with an outage probability of 0.5.
Oteri, Vincent Akpojevwe - One of the best experts on this subject based on the ideXlab platform.
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Drilling Optimization : drill bit Performance Optimization using DROPS simulator ( Ekofisk/Eldfisk Field)
University of Stavanger Norway, 2010Co-Authors: Oteri, Vincent AkpojevweAbstract:Master's thesis in Petroleum engineeringTwo drilled Wells: Well A and Well B were analysed under the following input data; drilling parameter, survey data, lithology data and bit information using DROPS simulator to showcase the bit Performance Optimization potentials. Apparent Rock Strength Logs (ARSL) were generated automatically by the simulator for the two drilled wells to give an idea of how hard is the formatiom and the rate of penetration possible for the bits. Interesting plots of the Apparent Rock Strength, Rate of Penetration, Weight on Bit, Revolution per minute, pump flow rate, Plastic Viscosity, mud Weight and Bit wear versus depth for the Well A and Well B were expressly presented in this project work. Appreciable cost per foot savings was made after the bit Performance Optimization simulation have been performed and a much more better savings could have been made if actual figures and parameters were used rather than assumed. A better bit selection was made using ROP, drilling time, bit wear constant ( automatic evaluation by DROPS simulatior), bit cost and cost per foot for selection criteria. Bit hydraulics analysis as relevant to cutting removal was adequately explained and evaluated for each bit used during the drilling in the bit Performance Optimization using the DROPS simulator
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Drilling Optimization : drill bit Performance Optimization using DROPS simulator ( Ekofisk/Eldfisk Field)
University of Stavanger Norway, 2010Co-Authors: Oteri, Vincent AkpojevweAbstract:Two drilled Wells: Well A and Well B were analysed under the following input data; drilling parameter, survey data, lithology data and bit information using DROPS simulator to showcase the bit Performance Optimization potentials. Apparent Rock Strength Logs (ARSL) were generated automatically by the simulator for the two drilled wells to give an idea of how hard is the formatiom and the rate of penetration possible for the bits. Interesting plots of the Apparent Rock Strength, Rate of Penetration, Weight on Bit, Revolution per minute, pump flow rate, Plastic Viscosity, mud Weight and Bit wear versus depth for the Well A and Well B were expressly presented in this project work. Appreciable cost per foot savings was made after the bit Performance Optimization simulation have been performed and a much more better savings could have been made if actual figures and parameters were used rather than assumed. A better bit selection was made using ROP, drilling time, bit wear constant ( automatic evaluation by DROPS simulatior), bit cost and cost per foot for selection criteria. Bit hydraulics analysis as relevant to cutting removal was adequately explained and evaluated for each bit used during the drilling in the bit Performance Optimization using the DROPS simulator
