Spectrum-Efficient QoS-Aware Resource Assignment for FFR-Based D2D-Enabled Heterogeneous Networks
Bibliographic record
Abstract
Small cells and Device-to-Device (D2D) communication can improve the coverage and capacity of cellular systems, thereby enabling an enriched customer experience. To optimize that experience, this paper considers a problem of uplink weighted sum rate maximization for a single cell heterogeneous network containing macro cellular users, D2D users and femto-cell users. In particular, in order to control the co-tier and cross-tier interferences, a new Fractional Frequency Reuse (FFR) architecture is developed in which we seek to maximize a weighted sum rate, subject to minimum rate requirements and transmission power constraints. In order to tackle the resulting mixed integer nonlinear optimization problem, we develop a decomposition-based strategy. The proposed strategy solves the admission control, power control, and matching subproblems optimally, and allocates the remaining free sub-channels by using a heuristic suboptimal algorithm. Numerical results demonstrate that the proposed scheme, on average, achieves around 96% of the system sum rate of the optimal Branch and Bound method with much lower computational cost.
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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".