Cross-Layer Power Allocation in Nonorthogonal Multiple Access Systems for Statistical QoS Provisioning
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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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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 | 0.001 | 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 | 0.001 |
| 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 it