Power Allocation for Cooperative Non-Orthogonal Multiple Access Systems
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Bibliographic record
Abstract
Cooperative non-orthogonal multiple access (NOMA) has attracted more and more attentions recently, in which NOMA-strong users play as relays to help the data transmission of NOMA-weak users. Different from existing works, we study the problem of power allocation for cooperative NOMA systems with half-duplex relaying mode. From a fairness standpoint, our proposed scheme aims at maximizing the minimum achievable user rate in a paired user group. More specifically, we divide the cooperative NOMA systems into two categories, i.e., fixed relaying scheme and adaptive relaying scheme. Fixed relaying scheme means the transmit power at the relay node, namely , is a given fixed constant while adaptive relaying scheme implies that can adapt to channel conditions according to our strategy. It is shown that the formulated power allocation problem for fixed relaying scheme is quasi-concave while the problem for adaptive relaying scheme is not. Hence, we firstly solve the former problem using a bisection algorithm by transforming it into a sequence of convex feasibility problems. Then, relying on the derived results of fixed relaying scheme, we find that the problem for adaptive relaying scheme can be converted into a univariate function about , in which the optimum can be also obtained by a similar bisection procedure. Numerical results reveal that the proposed adaptive relaying scheme always outperforms the proposed fixed relaying scheme. In addition, we also show that the cooperative NOMA systems are especially appropriate for systems under low SNR environments or having significantly different fading coefficients between NOMA users.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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)
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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