Resource Allocation and Scheduling in Multi-Cell OFDMA Systems with Decode-and-Forward Relaying
Bibliographic record
Abstract
In this paper, we formulate resource allocation and scheduling for multi-cell orthogonal frequency division multiple access (OFDMA) systems with half-duplex decode-and-forward (DF) relaying as a joint optimization problem taking into account multi-cell interference and heterogeneous user data rate requirements. For efficient multi-cell interference mitigation, we incorporate a time slot allocation strategy into the problem formulation. We transform the resulting non-convex and combinatorial optimization problem into a standard convex problem by imposing an interference temperature constraint, which yields a lower bound for the original problem. Subsequently, the transformed optimization problem is solved by dual decomposition and a semi-distributed iterative resource allocation algorithm with closed-form power and subcarrier allocation policies is derived to maximize the average weighted system throughput (bit/s/Hz/base station). Simulation results illustrate that our proposed semi-distributed algorithm achieves practically the same performance as the centralized optimal solution of the original non-convex problem and provides a substantial performance gain compared to single-cell resource allocation and scheduling schemes.
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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) | 0.001 | 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".