Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systems
Abstract In 5G based communication systems, adaptive modulation and coding (AMC) is a key approach that optimizes data transmission by constantly modifying modulation schemes and error correction coding by the current channel circumstances. AMC’s main objective is to increase data transfer efficienc...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06509-0 |
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| author | A. Manikandan Rakesh Thoppaen Suresh Babu S Jai Ganesh T. Sanjay |
| author_facet | A. Manikandan Rakesh Thoppaen Suresh Babu S Jai Ganesh T. Sanjay |
| author_sort | A. Manikandan |
| collection | DOAJ |
| description | Abstract In 5G based communication systems, adaptive modulation and coding (AMC) is a key approach that optimizes data transmission by constantly modifying modulation schemes and error correction coding by the current channel circumstances. AMC’s main objective is to increase data transfer efficiency and reliability while adjusting to the frequently fluctuating and unexpected nature of wireless channels. However, the channel's quality can be impacted by several variables, including distance, fading, noise, and interference in the time-varying channel. Hence it won't be easy to approximate the channel state information (CSI) accurately for time-varying channels. This paper discusses the novel rate adaptation approach that leverages generative adversarial networks (GAN) along with AMC to ensure efficient and reliable data transfer in a dynamic and often challenging environment, that maximizes data throughput even under varying conditions and offers robustness under adverse ones. |
| format | Article |
| id | doaj-art-008dfdeb556f4979acae0170a18de14a |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-008dfdeb556f4979acae0170a18de14a2025-01-26T12:47:38ZengSpringerDiscover Applied Sciences3004-92612025-01-017211210.1007/s42452-025-06509-0Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systemsA. Manikandan0Rakesh Thoppaen Suresh Babu1S Jai Ganesh2T. Sanjay3Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa VidyapeethamFiserv IncDepartment of CSE-Cyber Security, Indian Institute of Information Technology KottayamJPMorgan Chase & Co.Abstract In 5G based communication systems, adaptive modulation and coding (AMC) is a key approach that optimizes data transmission by constantly modifying modulation schemes and error correction coding by the current channel circumstances. AMC’s main objective is to increase data transfer efficiency and reliability while adjusting to the frequently fluctuating and unexpected nature of wireless channels. However, the channel's quality can be impacted by several variables, including distance, fading, noise, and interference in the time-varying channel. Hence it won't be easy to approximate the channel state information (CSI) accurately for time-varying channels. This paper discusses the novel rate adaptation approach that leverages generative adversarial networks (GAN) along with AMC to ensure efficient and reliable data transfer in a dynamic and often challenging environment, that maximizes data throughput even under varying conditions and offers robustness under adverse ones.https://doi.org/10.1007/s42452-025-06509-0AMCGANBit error rateMachine learningThroughput |
| spellingShingle | A. Manikandan Rakesh Thoppaen Suresh Babu S Jai Ganesh T. Sanjay Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systems Discover Applied Sciences AMC GAN Bit error rate Machine learning Throughput |
| title | Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systems |
| title_full | Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systems |
| title_fullStr | Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systems |
| title_full_unstemmed | Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systems |
| title_short | Generative adversarial networks based adaptive modulation and coding for next-generation 5G communication systems |
| title_sort | generative adversarial networks based adaptive modulation and coding for next generation 5g communication systems |
| topic | AMC GAN Bit error rate Machine learning Throughput |
| url | https://doi.org/10.1007/s42452-025-06509-0 |
| work_keys_str_mv | AT amanikandan generativeadversarialnetworksbasedadaptivemodulationandcodingfornextgeneration5gcommunicationsystems AT rakeshthoppaensureshbabu generativeadversarialnetworksbasedadaptivemodulationandcodingfornextgeneration5gcommunicationsystems AT sjaiganesh generativeadversarialnetworksbasedadaptivemodulationandcodingfornextgeneration5gcommunicationsystems AT tsanjay generativeadversarialnetworksbasedadaptivemodulationandcodingfornextgeneration5gcommunicationsystems |