Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems
Multi-tier computing can enhance the task computation by multi-tier computing nodes. In this paper, we propose a cell-free massive multiple-input multiple-output (MIMO) aided computing system by deploying multi-tier computing nodes to improve the computation performance. At first, we investigate the...
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sg-ntu-dr.10356-1725632023-12-13T01:54:16Z Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems Wang, Kunlun Niyato, Dusit Chen, Wen Nallanathan, Arumugam School of Computer Science and Engineering Engineering::Computer science and engineering Energy and Delay Tradeoff Cell-Free Massive MIMO Systems Multi-tier computing can enhance the task computation by multi-tier computing nodes. In this paper, we propose a cell-free massive multiple-input multiple-output (MIMO) aided computing system by deploying multi-tier computing nodes to improve the computation performance. At first, we investigate the computational latency and the total energy consumption for task computation, regarded as total cost. Then, we formulate a total cost minimization problem to design the bandwidth allocation and task allocation, while considering realistic heterogenous delay requirements of the computational tasks. Due to the binary task allocation variable, the formulated optimization problem is non-convex. Therefore, we solve the bandwidth allocation and task allocation problem by decoupling the original optimization problem into bandwidth allocation and task allocation subproblems. As the bandwidth allocation problem is a convex optimization problem, we first determine the bandwidth allocation for given task allocation strategy, followed by conceiving the traditional convex optimization strategy to obtain the bandwidth allocation solution. Based on the asymptotic property of received signal-to-interference-plus-noise ratio (SINR) under the cell-free massive MIMO setting and bandwidth allocation solution, we formulate a dual problem to solve the task allocation subproblem by relaxing the binary constraint with Lagrange partial relaxation for heterogenous task delay requirements. At last, simulation results are provided to demonstrate that our proposed task offloading scheme performs better than the benchmark schemes, where the minimum-cost optimal offloading strategy for heterogeneous delay requirements of the computational tasks may be controlled by the asymptotic property of the received SINR in our proposed cell-free massive MIMO-aided multi-tier computing systems. This work was supported by the National Key Project under Grant 2020YFB1807700. 2023-12-13T01:54:16Z 2023-12-13T01:54:16Z 2023 Journal Article Wang, K., Niyato, D., Chen, W. & Nallanathan, A. (2023). Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems. IEEE Journal On Selected Areas in Communications, 41(7), 2000-2012. https://dx.doi.org/10.1109/JSAC.2023.3280965 0733-8716 https://hdl.handle.net/10356/172563 10.1109/JSAC.2023.3280965 2-s2.0-85161000444 7 41 2000 2012 en IEEE Journal on Selected Areas in Communications © 2023 IEEE. All rights reserved. |
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Engineering::Computer science and engineering Energy and Delay Tradeoff Cell-Free Massive MIMO Systems Wang, Kunlun Niyato, Dusit Chen, Wen Nallanathan, Arumugam Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems |
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Multi-tier computing can enhance the task computation by multi-tier computing nodes. In this paper, we propose a cell-free massive multiple-input multiple-output (MIMO) aided computing system by deploying multi-tier computing nodes to improve the computation performance. At first, we investigate the computational latency and the total energy consumption for task computation, regarded as total cost. Then, we formulate a total cost minimization problem to design the bandwidth allocation and task allocation, while considering realistic heterogenous delay requirements of the computational tasks. Due to the binary task allocation variable, the formulated optimization problem is non-convex. Therefore, we solve the bandwidth allocation and task allocation problem by decoupling the original optimization problem into bandwidth allocation and task allocation subproblems. As the bandwidth allocation problem is a convex optimization problem, we first determine the bandwidth allocation for given task allocation strategy, followed by conceiving the traditional convex optimization strategy to obtain the bandwidth allocation solution. Based on the asymptotic property of received signal-to-interference-plus-noise ratio (SINR) under the cell-free massive MIMO setting and bandwidth allocation solution, we formulate a dual problem to solve the task allocation subproblem by relaxing the binary constraint with Lagrange partial relaxation for heterogenous task delay requirements. At last, simulation results are provided to demonstrate that our proposed task offloading scheme performs better than the benchmark schemes, where the minimum-cost optimal offloading strategy for heterogeneous delay requirements of the computational tasks may be controlled by the asymptotic property of the received SINR in our proposed cell-free massive MIMO-aided multi-tier computing systems. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Wang, Kunlun Niyato, Dusit Chen, Wen Nallanathan, Arumugam |
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Article |
author |
Wang, Kunlun Niyato, Dusit Chen, Wen Nallanathan, Arumugam |
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Wang, Kunlun |
title |
Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems |
title_short |
Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems |
title_full |
Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems |
title_fullStr |
Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems |
title_full_unstemmed |
Task-oriented delay-aware multi-tier computing in cell-free massive MIMO systems |
title_sort |
task-oriented delay-aware multi-tier computing in cell-free massive mimo systems |
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2023 |
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https://hdl.handle.net/10356/172563 |
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1787136486629441536 |