Joint Channel Bandwidth and Power Allocations for Downlink Non-Orthogonal Multiple Access Systems
Bibliographic record
Abstract
The advanced non-orthogonal multiple access (NOMA) has been considered as a promising scheme to satisfy the ultimate goals of future 5G cellular networks for providing ultra-high throughput and ultra-dense connections. By enabling a group of mobile users (MUs) to simultaneously share a same frequency channel and adopting successive interference cancellation to mitigate the co-channel interference, the NOMA can significantly improve the spectrum efficiency compared with the conventional orthogonal multiple access (OMA). However, due to cellular operators' limited and crowded spectrum resources, a critical question is how to properly size the channel bandwidth for the NOMA- enabled transmission to satisfy all MUs' traffic demands. In this paper, we propose a joint optimization scheme of bandwidth and power allocations for the NOMA- enabled downlink transmission, with the objective of minimizing the overall resource consumption cost that accounts for both the spectrum consumption cost and power consumption cost. In spite of the non-convexity nature of the joint optimization problem, we propose an efficient algorithm to compute the optimal bandwidth allocation and power allocation. Numerical results validate the proposed algorithm and the performance advantage of the proposed NOMA-enabled transmission in saving the overall resource consumption cost.
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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 | 0.001 | 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".