Optimization of Discrete Power and Resource Block Allocation for Achieving Maximum Energy Efficiency in OFDMA Networks
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Bibliographic record
Abstract
Most of the resource allocation literature on the energy-efficient orthogonal frequency division multiple access (OFDMA)-based wireless communication systems assume continuous power allocation/control, while, in practice, the power levels are discrete (such as in 3GPP LTE). This convenient continuous power assumption has mainly been due to either the limitations of the used optimization tools and/or the high computational complexity involved in addressing the more realistic discrete power allocation/control. In this paper, we introduce a new optimization framework to maximize the energy efficiency of the downlink transmission of cellular OFDMA networks subject to power budget and quality-of-service constraints, while considering discrete power and resource blocks (RBs) allocations. The proposed framework consists of two parts: 1) we model the predefined discrete power levels and RBs allocations by a single binary variable and 2) we propose a close-to-optimal semidefinite relaxation algorithm with Gaussian randomization to efficiently solve this non-convex combinatorial optimization problem with polynomial time complexity. We notice that a small number of power levels suffice to approach the energy efficiency performance of the continuous power allocation. Based on this observation, we propose an iterative suboptimal heuristic to further reduce the computational complexity. Simulation results show the effectiveness of the proposed schemes in maximizing the energy efficiency, while considering the practical discrete power levels.
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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 | 0.001 |
| 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 it