In this paper, we first consider the problem of distributed power control in a Full Duplex （FD） wireless network consisting of multiple pairs of nodes, within which each node needs to communicate with its correspond...In this paper, we first consider the problem of distributed power control in a Full Duplex （FD） wireless network consisting of multiple pairs of nodes, within which each node needs to communicate with its corresponding node. We aim to find the optimal transmition power for the FD transmitters such that the network-wide capacity is maximized. Based on the high Signal-to-Interference-Plus-Noise Ratio （SINR） approximation and a more general approximation method for logarithm functions, we develop effective distributed power control algorithms with the dual decomposition approach. We also extend the work to the general FD network scenario, which can be decomposed into subproblems of isolated nodes, paths, and cycles. The corresponding power control problem is then be solved with the distributed algorithm. The proposed algorithms are validated with simulation studies.展开更多
With the deteriorating effects resulting from global warming in many areas, geographically distributed data centers contribute greatly to carbon emissions, because the major energy supply is fossil fuels. Considering ...With the deteriorating effects resulting from global warming in many areas, geographically distributed data centers contribute greatly to carbon emissions, because the major energy supply is fossil fuels. Considering this issue, many geographically distributed data centers are attempting to use clean energy as their energy supply, such as fuel cells and renewable energy sources. However, not all workloads can be powered by a single power sources, since different workloads exhibit different characteristics. In this paper, we propose a fine-grained heterogeneous power distribution model with an objective of minimizing the total energy costs and the sum of the energy gap generated by the geographically distributed data centers powered by multiple types of energy resources. In order to achieve these two goals, we design a two-stage online algorithm to leverage the power supply of each energy source. In each time slot, we also consider a chance-constraint problem and use the Bernstein approximation to solve the problem. Finally, simulation results based on real-world traces illustrate that the proposed algorithm can achieve satisfactory performance.展开更多
基金This paper was presented in part at IEEE WCNC 2015, New Orleans, LA, USA, Mar. 2015 [1]. This work is supported in part by the US National Science Foundation under Grants CNS-1247955, and by the Wireless Engineering Research and Education Center （WEREC） at Auburn University, Auburn, AL, USA.
文摘In this paper, we first consider the problem of distributed power control in a Full Duplex （FD） wireless network consisting of multiple pairs of nodes, within which each node needs to communicate with its corresponding node. We aim to find the optimal transmition power for the FD transmitters such that the network-wide capacity is maximized. Based on the high Signal-to-Interference-Plus-Noise Ratio （SINR） approximation and a more general approximation method for logarithm functions, we develop effective distributed power control algorithms with the dual decomposition approach. We also extend the work to the general FD network scenario, which can be decomposed into subproblems of isolated nodes, paths, and cycles. The corresponding power control problem is then be solved with the distributed algorithm. The proposed algorithms are validated with simulation studies.
基金supported in part by National Natural Science Foundation of China (No. 61772286, No. 61802208)China Postdoctoral Science Foundation(No. 2019M651923)+2 种基金Natural Science Foundation of Jiangsu Province of China(No. BK20191381)Primary Research&Development Plan of Jiangsu Province(No. BE2019742)Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 18KJB520036)。
文摘With the deteriorating effects resulting from global warming in many areas, geographically distributed data centers contribute greatly to carbon emissions, because the major energy supply is fossil fuels. Considering this issue, many geographically distributed data centers are attempting to use clean energy as their energy supply, such as fuel cells and renewable energy sources. However, not all workloads can be powered by a single power sources, since different workloads exhibit different characteristics. In this paper, we propose a fine-grained heterogeneous power distribution model with an objective of minimizing the total energy costs and the sum of the energy gap generated by the geographically distributed data centers powered by multiple types of energy resources. In order to achieve these two goals, we design a two-stage online algorithm to leverage the power supply of each energy source. In each time slot, we also consider a chance-constraint problem and use the Bernstein approximation to solve the problem. Finally, simulation results based on real-world traces illustrate that the proposed algorithm can achieve satisfactory performance.
