Dual Decomposition Method for Energy-Efficient Resource Allocation in D2D Communications Underlying Cellular Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we study the energy-efficient resource allocation for device-to-device (D2D) communication underlying cellular networks. Specifically, we aim to maximize the minimum weighted energy-efficiency (EE) of D2D links while guaranteeing the minimum data rates of the cellular links. This design, therefore, guarantees fairness for D2D links and quality-of-service (QoS) for cellular links. Toward this end, we first characterize the optimal power allocation for cellular links based on which the original resource allocation problem can be transformed into the joint sub-channel and power allocation problem for D2D links. We then propose a dual decomposition based algorithm to solve the resource allocation problem in the dual domain. Theoretical analysis demonstrates that the proposed algorithm achieves strong performance guarantee. Numerical studies show that the proposed algorithm achieves nearly optimal performance, and it performs much better than the spectrum-efficient algorithm.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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