Mobile Power Allocation Intelligent Optimization Algorithm for Cooperative NOMA Network Based on CBAM-BiLSTM
Bibliographic record
Abstract
The non-orthogonal multiple access (NOMA) technology can greatly improve the spectral efficiency of wireless communication systems. The incorporation of NOMA technology into a 5G mobile communication network has the potential to significantly improve communication performance. First, we establish an mobile cooperative NOMA multi-user network. The exact outage probability (OP) expressions are then derived, and the effect of the power allocation on OP performance is investigated. Finally, we design a CBAM-BiLSTM network and propose an intelligent power allocation optimization algorithm based on system efficiency and user fairness. The CBAM-BiLSTM network is a structure based on convolutional block attention mechanism (CBAM) and bidirectional long short term memory network (BiLSTM). CBAM performs feature selection and weights the spatial and channel dimensions of feature maps to improve the network's classification accuracy. BiLSTM can fully utilize contextual information and handle long-term dependencies, thereby providing more comprehensive and accurate modeling capabilities and predictive performance. Simulation results indicate that, compared with the Transformer, ShuffleNetV2, and YOLOv5 algorithms, the CBAM-BiLSTM can obtain more accurate power allocation coefficients and improve system performance. Compared to ShuffleNetV2, CBAM-BiLSTM reduces mean square error (MSE) by 42.8%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".