Power Allocation in CoMP-Empowered C-NOMA Networks
Bibliographic record
Abstract
In this letter, the dynamic power allocation problem of a cellular network consisting of two adjacent and coordinating cells is investigated. The joint transmission coordinated multipoint (JT-CoMP) between the two-cell is introduced to assist users experiencing high inter-cell interference, where each cell invokes cooperative non orthogonal multiple access (C-NOMA) to serve its associated devices. Both effects of imperfect successive interference cancellation (SIC) and imperfect channel estimation are considered within the proposed scheme. A power allocation framework is formulated as an optimization problem with the objective of maximizing the network sum-rate while guaranteeing a certain quality-of-service (QoS) for each user. The formulated optimization problem is neither concave nor quasi-concave, which is difficult to be solved directly unless using heuristic methods, which comes with the expense of high computational complexity. To overcome this issue, a near-optimal closed-form expressions for the power allocation are derived. The simulation results show that our purposed scheme achieves an average sum-rate that is 3% less than the one of the optimal power control but it can save up to 99% in the computational time. In addition, the superiority of the proposed CoMP C-NOMA scheme is demonstrated when compared to the well known C-NOMA scheme.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".