Joint relay, subcarrier and power allocation for OFDMA-based femtocell networks
Bibliographic record
Abstract
Relaying in femtocell networks is a promising and economically viable option to reduce the co-channel interference while improving indoor coverage and the network capacity in the next generation wireless networks. However, efficient relay selection as well as subcarrier and power allocation are critical in such networks when multiple users and multiple relays are considered. In this paper, an optimal resource (relay, subcarrier and power) allocation algorithm for co-channel deployed orthogonal frequency division multiple access (OFDMA) based femtocell systems is proposed. The resource allocation problem is formulated as a joint relay, subcarrier and power allocation problem with the objective of maximizing the sum of the weighted rates of the femtocell system subject to protecting the macrocell network's communications. Due to the non-convex nature of the original resource allocation problem, we obtain an optimal solution for the original problem by solving a relaxed problem via dual decomposition. Simulation results demonstrate that our proposed resource allocation algorithm outperforms the resource allocation algorithms proposed in literature by achieving higher throughput at the expense of a slight increment of the system complexity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".