Hybrid Half-Duplex/Full-Duplex Cooperative Non-Orthogonal Multiple Access With Transmit Power Adaptation
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Bibliographic record
Abstract
Power allocation is an important issue in order to optimize the performance of non-orthogonal multiple access (NOMA) systems. However, the power allocation problem for cooperative NOMA systems has not been well investigated. In this paper, we investigate the power allocation problems for half-duplex cooperative NOMA (HD-CNOMA) and full-duplex cooperative NOMA (FD-CNOMA) systems, respectively. From the fairness standpoint, the optimization problem for each system is formulated to maximize the minimum achievable user rate in a NOMA user pair. Even though both of the formulated problems are neither concave nor quasi-concave, the optimal closed-form solutions of both cases are still obtained with the proposed two-step method. First, we transform the initial problem into a quasi-concave problem by treating the relay transmit power, namely R, as a constant, and then solve the obtained quasiconcave problem. Second, we convert the original problem into a univariate problem of R based on the results of the first step, and eventually obtain the optimal power allocation. In addition, a hybrid half/full-duplex cooperative NOMA scheme, which dynamically switches between the HD-CNOMA and FD-CNOMA mode, is proposed. After that, a relay selection scheme is also investigated to extend the hybrid scheme into general networks with multiple users. Numerical results demonstrate that the proposed hybrid relaying scheme can achieve a significant performance improvement with respect to the conventional NOMA, HD-CNOMA, and FD-CNOMA scheme.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 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