| Autor: |
Anwar, Khoirul, Alias, Mohamad Yusoff Bin |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2906 Issue 1, p1-8, 8p |
| Abstrakt: |
This paper proposes quantum machine learning (QML) to assist fast demapping of low order modulations of future wireless communications beyond the fifth generation (5G), where the 5G new radio (NR) demappers are used as examples since standards for 5G-Advanced and the sixth generation (6G) are still unavailable. We demonstrate that QML circuit can be constructed to solve the problem of demapping involving many points, which is in general difficult and intractable, especially when the number of modulation constellation points are significantly large. In this paper, we use an amplitude encoding technique to map the received signal constellations, followed by data processing prior to the measurement, for further processing. The proposed QML circuit is confirmed to be able to demap successfully the signals of 5G NR, i.e., complex binary phase shift keying (C-BPSK) modulations with the training data of only the four complex symbols. These results are expected to stimulate other variational quantum algorithms (VQA) for higher order modulations of 5G-Advanced or 6G communications. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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