QoS‐aware energy‐efficient resource allocation in OFDM‐based heterogenous cellular networks
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Bibliographic record
Abstract
Summary Recently, in order to satisfy the heavy demands of network capacity brought about by the proliferation of wireless devices, service providers are increasingly deploying heterogeneous cellular networks (HetNets) for boosting the network coverage and capacity. In this paper, we present an iterative energy‐efficient scheduling scheme for downlink OFDM‐based HetNets with QoS consideration. We formulate the problem as a nonlinear fractional programming problem aiming to maximize the QoS‐aware energy efficiency (QEE) in HetNets. In order to solve this problem, we first transform it into a parametric programming problem, which takes QEE as an evolved parameter in the iterative procedure of iterative energy‐efficient scheduling scheme. In each iteration, for the given value of QEE, subchannel and power assignment subproblem is a nonlinear nondeterministic polynomial time‐hard problem. And hence, we adopt dual decomposition method for obtaining the optimal assignment of subchannels and power of the subproblem for the given value of QEE. Simulation results depict that both outer QEE parameter search and inner subgradient search can converge in a few iterations, and the resultant solutions outperform the equal power allocation scheme [1] and capacity maximization scheme [2] in terms of QEE. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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