Delay-Tolerant Resource Allocation for D2D Communication Using Matching Theory
Bibliographic record
Abstract
Establishing direct communication between cellular devices in proximity brings benefits for the overall system sum-rate while generating interference. To deal with this additional interference efficiently, a low complexity uplink resource allocation algorithm for device-to-device (D2D) communication has been proposed in the underlying cellular networks by considering application-level requirements and request time-out. The related resource allocation is formulated as a non-linear optimization problem to maximize the system weighted sum-rate. An explicitly distributed coordination between different sources of channel state information (CSI) is introduced to avoid gathering all CSI information at the Base Station (BS) thus leading to significantly reduced overhead. Also, users' data-rate probability density function (pdf) is theoretically derived to determine approximately, for how long a D2D pair should wait to get the requested service. To assist the BS in collecting D2D requests, a frame structure has been proposed. Through simulation, it has been demonstrated that the proposed scheme outperforms the scheme with random allocation. Also, it has been confirmed that the proposed algorithm and the Exhaustive search strategy performs very close to the Exhaustive search strategy that exhibits optimal performance when the D2D pairs are fewer in number.
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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".