The Experts below are selected from a list of 24414 Experts worldwide ranked by ideXlab platform
Shijun Lin - One of the best experts on this subject based on the ideXlab platform.
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sum rate optimization for device to device communications over rayleigh fading channel
Vehicular Technology Conference, 2017Co-Authors: Shijun LinAbstract:In this paper, we investigate the sum-rate maximization in the Device-to-Device (D2D) communication underlaying cellular networks, where many cellular users (CUs) share the uplink Resource with the D2D pairs. We show that the system sum- rate maximization problem can be formulated as a mixed integer non-linear programming problem, which is NP-hard in general. We circumvent this difficulty by applying the optimization decomposition: 1) Given a Resource Allocation Policy, we derive the optimal Signal to Interference plus Noise Ratio (SINR) threshold to maximize the system sum-rate. 2) We then propose a coalition game approach to further optimize the Resource Allocation Policy, and prove that the proposed coalition game approach can converge to the Nash-stable partition in finite time. Simulation results show that 1) the performance of the coalition game is close to the exhaustive search, but its run-time is much shorter than the exhaustive search; 2) compared with several other Resource Allocation policies, the coalition game can achieve an average sum-rate improvement of 13%-173%, and has the best Resource sharing fairness.
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VTC Spring - Sum-Rate Optimization for Device-to-Device Communications over Rayleigh Fading Channel
2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 2017Co-Authors: Shijun LinAbstract:In this paper, we investigate the sum-rate maximization in the Device-to-Device (D2D) communication underlaying cellular networks, where many cellular users (CUs) share the uplink Resource with the D2D pairs. We show that the system sum- rate maximization problem can be formulated as a mixed integer non-linear programming problem, which is NP-hard in general. We circumvent this difficulty by applying the optimization decomposition: 1) Given a Resource Allocation Policy, we derive the optimal Signal to Interference plus Noise Ratio (SINR) threshold to maximize the system sum-rate. 2) We then propose a coalition game approach to further optimize the Resource Allocation Policy, and prove that the proposed coalition game approach can converge to the Nash-stable partition in finite time. Simulation results show that 1) the performance of the coalition game is close to the exhaustive search, but its run-time is much shorter than the exhaustive search; 2) compared with several other Resource Allocation policies, the coalition game can achieve an average sum-rate improvement of 13%-173%, and has the best Resource sharing fairness.
Jamie Evans - One of the best experts on this subject based on the ideXlab platform.
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Average Transmission Success Probability Bound for SWIPT Relay Networks
arXiv: Signal Processing, 2019Co-Authors: Bhathiya Pilanawithana, Saman Atapattu, Jamie EvansAbstract:Wireless energy transferring technology offers a constant and instantaneous power for low-power applications such as Internet of Things (IoT) to become an affordable reality. This paper considers simultaneous wireless information and power transfer (SWIPT) over a dual-hop decode-and-forward (DF) relay network with the power-splitting (PS) energy harvesting protocol at the relay. The relay is equipped with a finite capacity battery. The system performance, which is characterized by the average success probability of source to destination transmission, is a function of the Resource Allocation Policy that selects the PS ratio and the transmit energy of the relay. We develop a mathematical framework to find an upper bound for the maximum the average success probability. The upper bound is formulated by a discrete state space Markov decision problem (MDP) and make use of a Policy iteration algorithm to calculate it.
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WCNC - Average Transmission Success Probability Bound for SWIPT Relay Networks
2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019Co-Authors: Bhathiya Pilanawithana, Saman Atapattu, Jamie EvansAbstract:Wireless energy transferring technology offers a constant and instantaneous power for low-power applications such as Internet of Things (IoT) to become an affordable reality. This paper considers simultaneous wireless information and power transfer (SWIPT) over a dual-hop decode-and-forward (DF) relay network with the power-splitting (PS) energy harvesting protocol at the relay. The relay is equipped with a finite capacity battery. The system performance, which is characterized by the average success probability of source to destination transmission, is a function of the Resource Allocation Policy that selects the PS ratio and the transmit energy of the relay. We develop a mathematical framework to find an upper bound for the maximum the average success probability. The upper bound is formulated by a discrete state space Markov decision problem (MDP) and make use of a Policy iteration algorithm to calculate it.
Bhathiya Pilanawithana - One of the best experts on this subject based on the ideXlab platform.
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Average Transmission Success Probability Bound for SWIPT Relay Networks
arXiv: Signal Processing, 2019Co-Authors: Bhathiya Pilanawithana, Saman Atapattu, Jamie EvansAbstract:Wireless energy transferring technology offers a constant and instantaneous power for low-power applications such as Internet of Things (IoT) to become an affordable reality. This paper considers simultaneous wireless information and power transfer (SWIPT) over a dual-hop decode-and-forward (DF) relay network with the power-splitting (PS) energy harvesting protocol at the relay. The relay is equipped with a finite capacity battery. The system performance, which is characterized by the average success probability of source to destination transmission, is a function of the Resource Allocation Policy that selects the PS ratio and the transmit energy of the relay. We develop a mathematical framework to find an upper bound for the maximum the average success probability. The upper bound is formulated by a discrete state space Markov decision problem (MDP) and make use of a Policy iteration algorithm to calculate it.
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WCNC - Average Transmission Success Probability Bound for SWIPT Relay Networks
2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019Co-Authors: Bhathiya Pilanawithana, Saman Atapattu, Jamie EvansAbstract:Wireless energy transferring technology offers a constant and instantaneous power for low-power applications such as Internet of Things (IoT) to become an affordable reality. This paper considers simultaneous wireless information and power transfer (SWIPT) over a dual-hop decode-and-forward (DF) relay network with the power-splitting (PS) energy harvesting protocol at the relay. The relay is equipped with a finite capacity battery. The system performance, which is characterized by the average success probability of source to destination transmission, is a function of the Resource Allocation Policy that selects the PS ratio and the transmit energy of the relay. We develop a mathematical framework to find an upper bound for the maximum the average success probability. The upper bound is formulated by a discrete state space Markov decision problem (MDP) and make use of a Policy iteration algorithm to calculate it.
Tieqiao Liu - One of the best experts on this subject based on the ideXlab platform.
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Resource Allocation Policy based on trust in the multi cloud environment
Systems Man and Cybernetics, 2017Co-Authors: Jie Yang, Haibin Zhu, Xianjun Zhu, Yi Liu, Linyuan Liu, Tieqiao LiuAbstract:Cloud computing is growing rapidly due to its features of low cost, space saving, sharing etc., yet security issues are still a major concern. Multi-cloud computing technology was developed, in part, to address security concerns. One technique could use the Shamir's secret sharing method which is based on risk diversification. This work offers a solution strategy allowing users to allocate their Resources to different cloud providers. In addition, we use trust values to measure a user's security when using cloud providers. Such values are used in contrast to the risk that user's Resources may be leaked. The strategy is to find an appropriate plan whose trust value, from the user's perspective, is maximized by using a genetic algorithm. Experimental results indicate that the proposed solution strategy is effective and efficient.
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SMC - Resource Allocation Policy based on trust in the multi-cloud environment
2017 IEEE International Conference on Systems Man and Cybernetics (SMC), 2017Co-Authors: Jie Yang, Haibin Zhu, Xianjun Zhu, Yi Liu, Linyuan Liu, Tieqiao LiuAbstract:Cloud computing is growing rapidly due to its features of low cost, space saving, sharing etc., yet security issues are still a major concern. Multi-cloud computing technology was developed, in part, to address security concerns. One technique could use the Shamir's secret sharing method which is based on risk diversification. This work offers a solution strategy allowing users to allocate their Resources to different cloud providers. In addition, we use trust values to measure a user's security when using cloud providers. Such values are used in contrast to the risk that user's Resources may be leaked. The strategy is to find an appropriate plan whose trust value, from the user's perspective, is maximized by using a genetic algorithm. Experimental results indicate that the proposed solution strategy is effective and efficient.
Shivnath Babu - One of the best experts on this subject based on the ideXlab platform.
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mifo a query semantic aware Resource Allocation Policy
International Conference on Management of Data, 2019Co-Authors: Prajakta Kalmegh, Shivnath BabuAbstract:Data Analytics Frameworks encourage sharing of clusters for execution of mixed workloads by promising fairness and isolation along with high performance and Resource utilization. However, concurrent query executions on such shared clusters result in increased queue and Resource waiting times for queries affecting their overall performance. MIFO is a dataflow aware scheduling Policy that mitigates the impacts due to queue and Resource contentions by reducing the waiting times for queries near completion. We present heuristics that exploit query semantics to proactively trigger MIFO-based Allocations in a workload. Our experiments on Apache Spark using TPCDS benchmark show that compared to a FAIR Policy, MIFO provides an improved mean response time, reduced makespan of the workload and average speedup between 1.2x-2.7x in highly concurrent setting with only a momentary deviation in fairness.
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SIGMOD Conference - MIFO: A Query-Semantic Aware Resource Allocation Policy
Proceedings of the 2019 International Conference on Management of Data, 2019Co-Authors: Prajakta Kalmegh, Shivnath BabuAbstract:Data Analytics Frameworks encourage sharing of clusters for execution of mixed workloads by promising fairness and isolation along with high performance and Resource utilization. However, concurrent query executions on such shared clusters result in increased queue and Resource waiting times for queries affecting their overall performance. MIFO is a dataflow aware scheduling Policy that mitigates the impacts due to queue and Resource contentions by reducing the waiting times for queries near completion. We present heuristics that exploit query semantics to proactively trigger MIFO-based Allocations in a workload. Our experiments on Apache Spark using TPCDS benchmark show that compared to a FAIR Policy, MIFO provides an improved mean response time, reduced makespan of the workload and average speedup between 1.2x-2.7x in highly concurrent setting with only a momentary deviation in fairness.
