Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems
Aiming at the problem of low throughput and energy efficiency caused by limited wireless resources, huge power consumption, and mutual constraints between energy efficiency and system capacity in millimeter-wave large-scale multiple-input multiple-output systems, a resource co-optimization method ba...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-10-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024217/ |
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| author | LIU Qingli LI Xiaoyu LI Rui |
| author_facet | LIU Qingli LI Xiaoyu LI Rui |
| author_sort | LIU Qingli |
| collection | DOAJ |
| description | Aiming at the problem of low throughput and energy efficiency caused by limited wireless resources, huge power consumption, and mutual constraints between energy efficiency and system capacity in millimeter-wave large-scale multiple-input multiple-output systems, a resource co-optimization method based on deep reinforcement learning was proposed. The method was adopted in a three-stage strategy, firstly, an RF beamformer was constructed to reduce the hardware cost and total power consumption through a small number of RF chains; secondly, a baseband precoder was designed using the effective channel state information; and finally, a two-tier deep reinforcement learning architecture was designed and applied to realize dynamic discrete bandwidth and continuous power resource allocation. Experimental results show that the proposed joint optimization method significantly improves the throughput and energy efficiency of the system compared with the single-stage all-digital precoding and hybrid precoding equal resource allocation methods and the particle swarm optimization-based resource allocation algorithm. |
| format | Article |
| id | doaj-art-149efcaf1583430ebc112d205b1785cb |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-149efcaf1583430ebc112d205b1785cb2025-01-15T03:34:04ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-10-0140395176739100Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systemsLIU QingliLI XiaoyuLI RuiAiming at the problem of low throughput and energy efficiency caused by limited wireless resources, huge power consumption, and mutual constraints between energy efficiency and system capacity in millimeter-wave large-scale multiple-input multiple-output systems, a resource co-optimization method based on deep reinforcement learning was proposed. The method was adopted in a three-stage strategy, firstly, an RF beamformer was constructed to reduce the hardware cost and total power consumption through a small number of RF chains; secondly, a baseband precoder was designed using the effective channel state information; and finally, a two-tier deep reinforcement learning architecture was designed and applied to realize dynamic discrete bandwidth and continuous power resource allocation. Experimental results show that the proposed joint optimization method significantly improves the throughput and energy efficiency of the system compared with the single-stage all-digital precoding and hybrid precoding equal resource allocation methods and the particle swarm optimization-based resource allocation algorithm.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024217/millimeter-wave massive MIMO systemresource allocationjoint optimization |
| spellingShingle | LIU Qingli LI Xiaoyu LI Rui Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems Dianxin kexue millimeter-wave massive MIMO system resource allocation joint optimization |
| title | Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems |
| title_full | Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems |
| title_fullStr | Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems |
| title_full_unstemmed | Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems |
| title_short | Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems |
| title_sort | deep reinforcement learning based resource joint optimization for millimeter wave massive mimo systems |
| topic | millimeter-wave massive MIMO system resource allocation joint optimization |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024217/ |
| work_keys_str_mv | AT liuqingli deepreinforcementlearningbasedresourcejointoptimizationformillimeterwavemassivemimosystems AT lixiaoyu deepreinforcementlearningbasedresourcejointoptimizationformillimeterwavemassivemimosystems AT lirui deepreinforcementlearningbasedresourcejointoptimizationformillimeterwavemassivemimosystems |