Joint User Association and Power Allocation for Hybrid Half-Duplex/Full-Duplex Relaying in Cellular Networks
Bibliographic record
Abstract
For hybrid half-duplex/full-duplex relaying in a relay-aided cellular network, a joint user association, relay mode selection, and power allocation scheme is proposed to maximize the energy efficiency under the minimum spectral efficiency requirement and transmission power constraints. This combinatorial optimization problem is a mixed-integer nonconvex one, which is difficult to be solved in its original form. To circumvent this issue, the problem is first transformed into an equivalent one whose objective function is in a parametric subtractive form. Then, the equivalent problem is reformulated as a convex one by relaxing index variables, converting the nonconvexity by the successive convex approximation, and constructing auxiliary variables. In this way, the equivalent nonconvex problem is approximated by a convex one in each iteration, and the dual decomposition technique is employed to solve the convex one. An iterative algorithm is further developed to reach the solution to the primal problem. Simulation results show that the performance of the iterative algorithm can be very close to the optimal solution obtained by the exclusive searching within only a few number of iterations. Moreover, the proposed scheme outperforms existing ones, which only adhere to either the user association or the relay mode selection but not both.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".