Adaptive pilot design for OFDM based on deep reinforcement learning
For orthogonal frequency division multiplexing (OFDM) systems, an adaptive pilot design algorithm based on deep reinforcement learning was proposed.The pilot design problem was formulated as a Markov decision process, where the index of pilot positions was defined as actions.A reward function based...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023169/ |
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| _version_ | 1841539220180566016 |
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| author | Qiaoshou LIU Xiong ZHOU Shuang LIU Yifeng DENG |
| author_facet | Qiaoshou LIU Xiong ZHOU Shuang LIU Yifeng DENG |
| author_sort | Qiaoshou LIU |
| collection | DOAJ |
| description | For orthogonal frequency division multiplexing (OFDM) systems, an adaptive pilot design algorithm based on deep reinforcement learning was proposed.The pilot design problem was formulated as a Markov decision process, where the index of pilot positions was defined as actions.A reward function based on mean squared error (MSE) reduction strategy was formulated, and deep reinforcement learning was employed to update the pilot positions.The pilot was adaptively and dynamically allocated based on channel conditions, thereby utilizing channel characteristics to combat channel fading.The simulation results show that the proposed algorithm has significantly improved channel estimation performance compared with the traditional pilot uniform allocation scheme under three typical multipath channels of 3GPP. |
| format | Article |
| id | doaj-art-9f15c4da8d964af1b8fcf370192cf3c1 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-9f15c4da8d964af1b8fcf370192cf3c12025-01-14T07:23:31ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-09-014410411459835914Adaptive pilot design for OFDM based on deep reinforcement learningQiaoshou LIUXiong ZHOUShuang LIUYifeng DENGFor orthogonal frequency division multiplexing (OFDM) systems, an adaptive pilot design algorithm based on deep reinforcement learning was proposed.The pilot design problem was formulated as a Markov decision process, where the index of pilot positions was defined as actions.A reward function based on mean squared error (MSE) reduction strategy was formulated, and deep reinforcement learning was employed to update the pilot positions.The pilot was adaptively and dynamically allocated based on channel conditions, thereby utilizing channel characteristics to combat channel fading.The simulation results show that the proposed algorithm has significantly improved channel estimation performance compared with the traditional pilot uniform allocation scheme under three typical multipath channels of 3GPP.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023169/OFDMdeep reinforcement learningMarkov decision processmultipath channel |
| spellingShingle | Qiaoshou LIU Xiong ZHOU Shuang LIU Yifeng DENG Adaptive pilot design for OFDM based on deep reinforcement learning Tongxin xuebao OFDM deep reinforcement learning Markov decision process multipath channel |
| title | Adaptive pilot design for OFDM based on deep reinforcement learning |
| title_full | Adaptive pilot design for OFDM based on deep reinforcement learning |
| title_fullStr | Adaptive pilot design for OFDM based on deep reinforcement learning |
| title_full_unstemmed | Adaptive pilot design for OFDM based on deep reinforcement learning |
| title_short | Adaptive pilot design for OFDM based on deep reinforcement learning |
| title_sort | adaptive pilot design for ofdm based on deep reinforcement learning |
| topic | OFDM deep reinforcement learning Markov decision process multipath channel |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023169/ |
| work_keys_str_mv | AT qiaoshouliu adaptivepilotdesignforofdmbasedondeepreinforcementlearning AT xiongzhou adaptivepilotdesignforofdmbasedondeepreinforcementlearning AT shuangliu adaptivepilotdesignforofdmbasedondeepreinforcementlearning AT yifengdeng adaptivepilotdesignforofdmbasedondeepreinforcementlearning |