A Low-Complexity Approach for Sum-Rate Maximization in Cooperative NOMA Enhanced Cellular Networks
Bibliographic record
Abstract
This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in a cellular downlink system. The system model consists of a base station (BS) serving multiple users, where users that have the capability of full-duplex (FD) communications can assist the transmissions between the BS and users with poor channel quality through device-to-device (D2D) communications. To maximize the achievable sum rate of the whole system while guaranteeing a certain quality of service (QoS) for all users, we formulate and solve a novel optimization problem that jointly determines the optimal D2D user pairing and the optimal power control scheme. The formulated problem is a mixed-integer non-linear program (MINLP), which has extremely high complexity. To overcome this issue, a two-step policy is proposed to solve the problem in polynomial time. First, we derive a closed-form expression of the optimal power control scheme that maximizes the sum rate of a given pair of users with a required QoS. Then, using the derived closed-form in the first step, we employ the Hungarian algorithm as the pairing policy in multi-user settings. Our simulation results show that the proposed scheme prevails some previously proposed heuristic approach for the given problem.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".