Optimal Dual-Connectivity Traffic Offloading in Energy-Harvesting Small-Cell Networks
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Bibliographic record
Abstract
Traffic offloading through heterogenous small-cell networks (HSCNs) has been envisioned as a cost-efficient approach to accommodate the tremendous traffic growth in cellular networks. In this paper, we investigate an energy-efficient dual-connectivity (DC) enabled traffic offloading through HSCNs, in which small cells are powered in a hybrid manner including both the conventional on-grid power-supply and renewable energy harvested from environment. To achieve a flexible traffic offloading, the emerging DC-enabled traffic offloading in 3GPP specification allows each mobile user (MU) to simultaneously communicate with a macro cell and offload data through a small cell. In spite of saving the on-grid power consumption, powering traffic offloading by energy harvesting (EH) might lead to quality of service degradation, e.g., when the EH power-supply fails to support the required offloading rate. Thus, to reap the benefits of the DC-capability and the EH power-supply, we propose a joint optimization of traffic scheduling and power allocation that aims at minimizing the total on-grid power consumption of macro and small cells, while guaranteeing each served MU's traffic requirement. We start by studying a representative case of one small cell serving a group of MUs. In spite of the non-convexity of the formulated joint optimization problem, we exploit its layered structure and propose an algorithm that efficiently computes the optimal offloading solution. We further study the scenario of multiple small cells, and investigate how the small cells select different MUs for maximizing the system-wise reward that accounts for the revenue for offloading the MUs' traffic and the cost of total on-grid power consumption of all cells. We also propose an efficient algorithm to find the optimal MU-selection solution. Numerical results are provided to validate our proposed algorithms and show the advantage of our proposed DC-enabled traffic offloading through the EH-powered small cells.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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