Cross-Layer Power Allocation in Nonorthogonal Multiple Access Systems for Statistical QoS Provisioning
Bibliographic record
Abstract
Power allocation is a critical issue in the physical layer of power-domain nonorthogonal multiple access (NOMA) systems. However, existing power allocation schemes have not considered the delay quality of service (QoS) requirement in the datalink layer of users, and hence may not ensure the desired delay QoS requested by the services in the upper layers. Different from existing works, we apply the statistical QoS theory into NOMA systems and formulate the physical-datalink cross-layer power allocation problem as a stochastic optimization problem under different delay QoS constraints. Also, we show that the formulated problem is quasi-concave and propose a bisection-based cross-layer power allocation algorithm. Simulation results show that the proposed algorithm is able to converge to the optimal solution obtained by exhaustive search. Also, the proposed scheme outperforms existing fixed NOMA and time-division multiple access based schemes in terms of max-min effective capacity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".