The Experts below are selected from a list of 4341 Experts worldwide ranked by ideXlab platform
Robert Schober - One of the best experts on this subject based on the ideXlab platform.
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resource allocation for multi user downlink miso ofdma urllc systems
IEEE Transactions on Communications, 2020Co-Authors: Walid R. Ghanem, Vahid Jamali, Yan Sun, Robert SchoberAbstract:This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short Packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, Packet Error Probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.
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Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems.
arXiv: Information Theory, 2019Co-Authors: Walid R. Ghanem, Vahid Jamali, Yan Sun, Robert SchoberAbstract:This paper considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short Packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users' number of transmitted bits, Packet Error Probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity resource allocation algorithm to strike a balance between performance and complexity. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon's capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they may violate the users' delay constraints.
Walid R. Ghanem - One of the best experts on this subject based on the ideXlab platform.
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resource allocation for multi user downlink miso ofdma urllc systems
IEEE Transactions on Communications, 2020Co-Authors: Walid R. Ghanem, Vahid Jamali, Yan Sun, Robert SchoberAbstract:This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short Packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, Packet Error Probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.
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Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems.
arXiv: Information Theory, 2019Co-Authors: Walid R. Ghanem, Vahid Jamali, Yan Sun, Robert SchoberAbstract:This paper considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short Packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users' number of transmitted bits, Packet Error Probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity resource allocation algorithm to strike a balance between performance and complexity. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon's capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they may violate the users' delay constraints.
Biplab Sikdar - One of the best experts on this subject based on the ideXlab platform.
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energy efficient transmission strategies for body sensor networks with energy harvesting
IEEE Transactions on Communications, 2010Co-Authors: Alireza Seyedi, Biplab SikdarAbstract:This paper addresses the problem of developing energy efficient transmission strategies for Body Sensor Networks (BSNs) with energy harvesting. It is assumed that multiple transmission modes that allow a tradeoff between the energy consumption and Packet Error Probability are available to the sensor nodes. Taking into account the energy harvesting capabilities of the nodes, decision policies are developed to determine the transmission mode to use at a given instant of time in order to maximize the quality of coverage. The problem is formulated as a Markov Decision Process (MDP) and the performance of the transmission policy thus derived is compared with that of energy balancing as well as aggressive policies. An upper bound on the performance of arbitrary policies, and lower bounds specific to energy balancing and aggressive policies are derived.
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energy efficient transmission strategies for body sensor networks with energy harvesting
Conference on Information Sciences and Systems, 2008Co-Authors: Alireza Seyedi, Biplab SikdarAbstract:This paper addresses the problem of developing energy efficient transmission strategies for Body Sensor Networks (BSNs) with energy harvesting capabilities. It is assumed that two transmission modes that allow a tradeoff between the energy consumption and Packet Error Probability are available to the sensors. Decision policies are developed to determine the transmission mode to use at a given instant of time in order to maximize the quality of coverage. The problem is formulated in a Markov Decision Process (MDP) framework and an upper bound on the performance of arbitrary policies is determined. Our results show that the quality of coverage associated with the MDP formulation outperforms the other policies.
Vahid Jamali - One of the best experts on this subject based on the ideXlab platform.
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resource allocation for multi user downlink miso ofdma urllc systems
IEEE Transactions on Communications, 2020Co-Authors: Walid R. Ghanem, Vahid Jamali, Yan Sun, Robert SchoberAbstract:This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short Packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, Packet Error Probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.
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Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems.
arXiv: Information Theory, 2019Co-Authors: Walid R. Ghanem, Vahid Jamali, Yan Sun, Robert SchoberAbstract:This paper considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short Packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users' number of transmitted bits, Packet Error Probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity resource allocation algorithm to strike a balance between performance and complexity. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon's capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they may violate the users' delay constraints.
Yan Sun - One of the best experts on this subject based on the ideXlab platform.
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resource allocation for multi user downlink miso ofdma urllc systems
IEEE Transactions on Communications, 2020Co-Authors: Walid R. Ghanem, Vahid Jamali, Yan Sun, Robert SchoberAbstract:This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short Packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, Packet Error Probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.
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Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems.
arXiv: Information Theory, 2019Co-Authors: Walid R. Ghanem, Vahid Jamali, Yan Sun, Robert SchoberAbstract:This paper considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short Packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users' number of transmitted bits, Packet Error Probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity resource allocation algorithm to strike a balance between performance and complexity. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon's capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they may violate the users' delay constraints.
