A Poisson Shot Noise Limited MMSE Precoding for Photon-Counting MIMO Systems with Reinforcement Learning
With the development of the Internet of Things (IoT), most communication systems are difficult to implement on a large scale due to their high complexity. Multiple-input multiple-output (MIMO) precoding is a generally used technique for improving the reliability of free-space optical (FSO) communica...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/19/10855 |
| _version_ | 1827722791616512000 |
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| author | Zihao Li Xiaolin Zhou Chengrui Wan Gang Du Yuequan Wang |
| author_facet | Zihao Li Xiaolin Zhou Chengrui Wan Gang Du Yuequan Wang |
| author_sort | Zihao Li |
| collection | DOAJ |
| description | With the development of the Internet of Things (IoT), most communication systems are difficult to implement on a large scale due to their high complexity. Multiple-input multiple-output (MIMO) precoding is a generally used technique for improving the reliability of free-space optical (FSO) communications, which is a key technology in the 6G era. However, traditional MIMO precoding schemes are typically designed based on the assumption of additive white Gaussian noise (AWGN). In this paper, we present a novel MIMO precoding method based on reinforcement learning (RL) that is specifically designed for the Poisson shot noise model. Unlike traditional MIMO precoding schemes, our proposed scheme takes into account the unique statistical characteristics of Poisson shot noise. Our approach achieves significant performance gains compared to existing MIMO precoding schemes. The proposed scheme can achieve the bit error rate (BER) of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></semantics></math></inline-formula> in a strong turbulence channel and exhibits superior robustness against imperfect channel state information (CSI). |
| first_indexed | 2024-03-10T21:49:51Z |
| format | Article |
| id | doaj.art-b5a90f32059c4d45b938f9c946b2c256 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:49:51Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b5a90f32059c4d45b938f9c946b2c2562023-11-19T14:05:07ZengMDPI AGApplied Sciences2076-34172023-09-0113191085510.3390/app131910855A Poisson Shot Noise Limited MMSE Precoding for Photon-Counting MIMO Systems with Reinforcement LearningZihao Li0Xiaolin Zhou1Chengrui Wan2Gang Du3Yuequan Wang4School of Information Science and Technology, Fudan University, Shanghai 200433, ChinaSchool of Information Science and Technology, Fudan University, Shanghai 200433, ChinaSchool of Information Science and Technology, Fudan University, Shanghai 200433, ChinaSchool of Information Science and Technology, Fudan University, Shanghai 200433, ChinaSchool of Information Science and Technology, Fudan University, Shanghai 200433, ChinaWith the development of the Internet of Things (IoT), most communication systems are difficult to implement on a large scale due to their high complexity. Multiple-input multiple-output (MIMO) precoding is a generally used technique for improving the reliability of free-space optical (FSO) communications, which is a key technology in the 6G era. However, traditional MIMO precoding schemes are typically designed based on the assumption of additive white Gaussian noise (AWGN). In this paper, we present a novel MIMO precoding method based on reinforcement learning (RL) that is specifically designed for the Poisson shot noise model. Unlike traditional MIMO precoding schemes, our proposed scheme takes into account the unique statistical characteristics of Poisson shot noise. Our approach achieves significant performance gains compared to existing MIMO precoding schemes. The proposed scheme can achieve the bit error rate (BER) of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></semantics></math></inline-formula> in a strong turbulence channel and exhibits superior robustness against imperfect channel state information (CSI).https://www.mdpi.com/2076-3417/13/19/10855precodingreinforcement learningFSOMIMOPoisson shot noise |
| spellingShingle | Zihao Li Xiaolin Zhou Chengrui Wan Gang Du Yuequan Wang A Poisson Shot Noise Limited MMSE Precoding for Photon-Counting MIMO Systems with Reinforcement Learning Applied Sciences precoding reinforcement learning FSO MIMO Poisson shot noise |
| title | A Poisson Shot Noise Limited MMSE Precoding for Photon-Counting MIMO Systems with Reinforcement Learning |
| title_full | A Poisson Shot Noise Limited MMSE Precoding for Photon-Counting MIMO Systems with Reinforcement Learning |
| title_fullStr | A Poisson Shot Noise Limited MMSE Precoding for Photon-Counting MIMO Systems with Reinforcement Learning |
| title_full_unstemmed | A Poisson Shot Noise Limited MMSE Precoding for Photon-Counting MIMO Systems with Reinforcement Learning |
| title_short | A Poisson Shot Noise Limited MMSE Precoding for Photon-Counting MIMO Systems with Reinforcement Learning |
| title_sort | poisson shot noise limited mmse precoding for photon counting mimo systems with reinforcement learning |
| topic | precoding reinforcement learning FSO MIMO Poisson shot noise |
| url | https://www.mdpi.com/2076-3417/13/19/10855 |
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