Partial NOMA-Based Resource Allocation for Fairness in LTE-U System
Bibliographic record
Abstract
In order to tackle the spectrum scarcity problem and enhance the spectrum efficiency, deploying LTE in unlicensed band (LTE-U) is an emerging technology for supporting massive connections in future networks. By taking into account of the coexistence between the LTE-U cellular user equipments (CUEs) and the legacy Wi-Fi stations (STAs) in the unlicensed band, a partial non-orthogonal multiple access (NOMA)-based scheme is proposed in this paper. By dividing all UEs into two groups and making the Wi-Fi STA as the UE with the weakest channel gain in its group, we can exploit the multiplexing gain of NOMA by introducing no extra modification to Wi-Fi STAs. Accordingly, a fairness-oriented resource allocation framework is formulated as a max-min problem to jointly optimize the inter-group time occupancy ratio and the intra-group power allocation when the guaranteed bit rate (GBR) requirements for each UE are considered. A modified two-dimensional bisection algorithm is proposed to search the optimal time occupancy ratio and the max-min rate in this coexisting network. Numerical results validate the effectiveness of the partial NOMA scheme and outperform the traditional orthogonal multiple access method, in terms of both efficiency and robustness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".