Energy-Efficient Resource Allocation for OFDMA Cellular Networks With User Cooperation and QoS Provisioning
Bibliographic record
Abstract
In this paper, an energy-efficient resource allocation scheme is designed for orthogonal frequency-division multiple-access cellular wireless networks with multiuser cooperation. Joint relay selection, subcarrier allocation and pairing, and power-allocation algorithms are developed with the objective of maximizing the energy efficiency of the system considering the quality-of-service requirements of the users. The optimization problem is a mixed-integer nonlinear program (MINLP), which is generally very difficult to solve in its original form. The energy efficiency metric is a fractional and nonlinear function, which complicates the problem further. We provide a novel optimization framework to solve such nonlinear and nonconvex optimization problems. The MINLP optimization problem is reformulated to a convex problem by relaxing the integer variables and by introducing the "Dinkelbach" method to tackle the nonlinear fractional objective function. We prove that our proposed solution of the relaxed problem is optimal and has integer values. Based on the dual-decomposition method, we propose a solution to the joint optimization problem, which is optimal and computationally efficient with polynomial-time complexity. Numerical results demonstrate the effectiveness of our proposed scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".