Channel Estimation for Reconfigurable Intelligent Surface-aided 6G NOMA Systems using CNN-based Quantum LSTM Model
Bibliographic record
Abstract
With the rapid development of communication applications, the integration of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) techniques has emerged as a promising approach to enhance connectivity and data transmission rate in future wireless networks. To successfully deploy RIS-NOMA aided 6G network, an accurate channel estimation is a crucial task. Quantum machine learning (QML) is a novel approach showing potential computational advantages in various problems of 6G wireless communications. However, its application, particularly in channel estimation, remains largely theoretical rather than adopted in practice. We propose a hybrid quantum-classical neural network model based on convolutional neural network (CNN) and quantum long short-term memory (QLSTM) for channel estimation in RIS-aided 6G NOMA system. Our results show that the proposed CNN-QLSTM model has a better channel prediction compared to its classical counterpart with regard to root mean square error (RMSE) and mean absolute error (MAE).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".