Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems
The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/1/309 |
| _version_ | 1797497593246253056 |
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| author | Muddasar Naeem Giuseppe De Pietro Antonio Coronato |
| author_facet | Muddasar Naeem Giuseppe De Pietro Antonio Coronato |
| author_sort | Muddasar Naeem |
| collection | DOAJ |
| description | The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented. |
| first_indexed | 2024-03-10T03:21:27Z |
| format | Article |
| id | doaj.art-bb69cd46d1a24202b28df5c28a5caf69 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:21:27Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bb69cd46d1a24202b28df5c28a5caf692023-11-23T12:20:13ZengMDPI AGSensors1424-82202021-12-0122130910.3390/s22010309Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) SystemsMuddasar Naeem0Giuseppe De Pietro1Antonio Coronato2Institute of High Performance Computing and Networking, National Research Council of Italy, 80131 Napoli, ItalyInstitute of High Performance Computing and Networking, National Research Council of Italy, 80131 Napoli, ItalyInstitute of High Performance Computing and Networking, National Research Council of Italy, 80131 Napoli, ItalyThe current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.https://www.mdpi.com/1424-8220/22/1/309reinforcement learningdeep learningMIMO systemssignalchannel estimationdetection communication |
| spellingShingle | Muddasar Naeem Giuseppe De Pietro Antonio Coronato Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems Sensors reinforcement learning deep learning MIMO systems signal channel estimation detection communication |
| title | Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems |
| title_full | Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems |
| title_fullStr | Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems |
| title_full_unstemmed | Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems |
| title_short | Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems |
| title_sort | application of reinforcement learning and deep learning in multiple input and multiple output mimo systems |
| topic | reinforcement learning deep learning MIMO systems signal channel estimation detection communication |
| url | https://www.mdpi.com/1424-8220/22/1/309 |
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