Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer
Abstract To better balance the spectral efficiency (SE) and energy efficiency (EE) in the massive multiple-input multiple output system with a large number of users (MaMIMO-LU), the SE-EE tradeoff is originally constructed as a constrained large-scale multi-objective problem (CLSMOP) for the power a...
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2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01620-y |
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| author | Qingzhu Wang Tianyang Li |
| author_facet | Qingzhu Wang Tianyang Li |
| author_sort | Qingzhu Wang |
| collection | DOAJ |
| description | Abstract To better balance the spectral efficiency (SE) and energy efficiency (EE) in the massive multiple-input multiple output system with a large number of users (MaMIMO-LU), the SE-EE tradeoff is originally constructed as a constrained large-scale multi-objective problem (CLSMOP) for the power allocation of users. To solve this CLSMOP, a constrained large-scale multi-objective evolutionary algorithm (CLSMOEA), considering the dimensionality reduction as well as the balance of objectives and constraints, is explored. The Lagrange multiplier is first used to construct a two-scale optimization model, bridging original large-scale decision space of variables and small-scale decision space of coefficients of Lagrange multiplier. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces emphasizing objectives and constraints, respectively. In summary, the optimization in large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in small-scale space helps the population cross the infeasible areas to approach constrained PF, and the GD-based reproduction of offspring further guarantees the solution convergence. Ten representative and state-of-the-art constrained multi-objective evolutionary algorithms (MOEAs) and unconstrained MOEA have been compared to the proposed CLSMOEA to demonstrate its effectiveness through comparative experiments on some well-known benchmark problems (with 1000 variables), and MaMIMO-LU (with 1024 antennas and 256, 512, and 1024 users). Experimental results show that the proposed CLSMOEA can obtain the best SE-EE tradeoff. |
| format | Article |
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| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
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| series | Complex & Intelligent Systems |
| spelling | doaj-art-4bb03d51de7341ec806b6d661ee663af2025-02-02T12:49:51ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111113210.1007/s40747-024-01620-ySpectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transferQingzhu Wang0Tianyang Li1School of Computer Science, Northeast Electric Power UniversitySchool of Computer Science, Northeast Electric Power UniversityAbstract To better balance the spectral efficiency (SE) and energy efficiency (EE) in the massive multiple-input multiple output system with a large number of users (MaMIMO-LU), the SE-EE tradeoff is originally constructed as a constrained large-scale multi-objective problem (CLSMOP) for the power allocation of users. To solve this CLSMOP, a constrained large-scale multi-objective evolutionary algorithm (CLSMOEA), considering the dimensionality reduction as well as the balance of objectives and constraints, is explored. The Lagrange multiplier is first used to construct a two-scale optimization model, bridging original large-scale decision space of variables and small-scale decision space of coefficients of Lagrange multiplier. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces emphasizing objectives and constraints, respectively. In summary, the optimization in large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in small-scale space helps the population cross the infeasible areas to approach constrained PF, and the GD-based reproduction of offspring further guarantees the solution convergence. Ten representative and state-of-the-art constrained multi-objective evolutionary algorithms (MOEAs) and unconstrained MOEA have been compared to the proposed CLSMOEA to demonstrate its effectiveness through comparative experiments on some well-known benchmark problems (with 1000 variables), and MaMIMO-LU (with 1024 antennas and 256, 512, and 1024 users). Experimental results show that the proposed CLSMOEA can obtain the best SE-EE tradeoff.https://doi.org/10.1007/s40747-024-01620-yMassive MIMOSpectralEnergy tradeoffConstrained largeScale multiObjective problem |
| spellingShingle | Qingzhu Wang Tianyang Li Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer Complex & Intelligent Systems Massive MIMO Spectral Energy tradeoff Constrained large Scale multi Objective problem |
| title | Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer |
| title_full | Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer |
| title_fullStr | Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer |
| title_full_unstemmed | Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer |
| title_short | Spectral-energy efficiency tradeoff of massive MIMO by a constrained large-scale multi-objective algorithm through decision transfer |
| title_sort | spectral energy efficiency tradeoff of massive mimo by a constrained large scale multi objective algorithm through decision transfer |
| topic | Massive MIMO Spectral Energy tradeoff Constrained large Scale multi Objective problem |
| url | https://doi.org/10.1007/s40747-024-01620-y |
| work_keys_str_mv | AT qingzhuwang spectralenergyefficiencytradeoffofmassivemimobyaconstrainedlargescalemultiobjectivealgorithmthroughdecisiontransfer AT tianyangli spectralenergyefficiencytradeoffofmassivemimobyaconstrainedlargescalemultiobjectivealgorithmthroughdecisiontransfer |