A Meta-Learning-Based Precoder Optimization Framework for Rate-Splitting Multiple Access
In this letter, we propose the use of a meta-learning based precoder optimization framework to directly optimize the Rate-Splitting Multiple Access (RSMA) precoders with partial Channel State Information at the Transmitter (CSIT). By exploiting the overfitting of the compact neural network to maximi...
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| Vydané v: | IEEE wireless communications letters Ročník 13; číslo 2; s. 347 - 351 |
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| Jazyk: | English |
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IEEE
01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | In this letter, we propose the use of a meta-learning based precoder optimization framework to directly optimize the Rate-Splitting Multiple Access (RSMA) precoders with partial Channel State Information at the Transmitter (CSIT). By exploiting the overfitting of the compact neural network to maximize the explicit Average Sum-Rate (ASR) expression, we effectively bypass the need for any other training data while minimizing the total running time. Numerical results reveal that the meta-learning based solution achieves similar ASR performance to conventional precoder optimization in medium-scale scenarios, and significantly outperforms sub-optimal low complexity precoder algorithms in the large-scale regime. |
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| AbstractList | In this letter, we propose the use of a meta-learning based precoder optimization framework to directly optimize the Rate-Splitting Multiple Access (RSMA) precoders with partial Channel State Information at the Transmitter (CSIT). By exploiting the overfitting of the compact neural network to maximize the explicit Average Sum-Rate (ASR) expression, we effectively bypass the need for any other training data while minimizing the total running time. Numerical results reveal that the meta-learning based solution achieves similar ASR performance to conventional precoder optimization in medium-scale scenarios, and significantly outperforms sub-optimal low complexity precoder algorithms in the large-scale regime. |
| Author | Cerna Loli, Rafael Clerckx, Bruno |
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| Cites_doi | 10.1109/TSP.2018.2866382 10.1109/COMST.2022.3191937 10.1109/TCOMM.2018.2842758 10.1109/WCNCW49093.2021.9420034 10.1109/LWC.2019.2954518 10.1109/LWC.2022.3186160 10.1109/TNNLS.2022.3165627 10.1109/TWC.2016.2543212 10.1109/TCOMM.2016.2603991 10.1109/TCOMM.2020.3014153 10.1186/s13638-018-1104-7 10.1109/TSP.2023.3238275 |
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| StartPage | 347 |
| SubjectTerms | Algorithms Artificial neural networks Channel state information Complexity theory Decoding Interference Learning Metalearning Multiple access Neural networks non-convex optimization Optimization partial channel state information at the transmitter (CSIT) Rate-splitting multiple access (RSMA) Splitting |
| Title | A Meta-Learning-Based Precoder Optimization Framework for Rate-Splitting Multiple Access |
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