Energy-Efficient Resource Allocation in Heterogeneous Small Cell Networks with WiFi Spectrum Sharing
Bibliographic record
Abstract
In this paper, we investigate the dynamic subchannel and power allocation in licensed/unlicensed spectrum sharing heterogeneous small cell networks with incomplete channel state information (CSI). We explore the formulated problem using the Lyapunov optimization method by considering co-tier interference and cross- tier interference in both licensed and unlicensed spectrums. The constraints of the minimum user quality of service (QoS), the maximum transmit power limit and the unique of subchannel allocation are also considered to achieve the optimal power and subchannel allocation. Based on the framework of Lyapunov optimization, the problem of energy efficient (EE) optimization can be broken down into three subproblems. Two of which are linear and the rest can be solved by introducing Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the EE-delay tradeoff.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".