QoS-Guaranteed User Association in HetNets via Semidefinite Relaxation
Bibliographic record
Abstract
The objective of this work is to determine a close-to-optimal user-to-base station (BS) association that maximizes the number of users served by the downlink of a heterogeneous network (HetNet). Such an association must not only ensure that the number of accommodated users is maximized but also that the network resources are efficiently utilized and the users' quality of service (QoS) demands are met. In its simplest form, the optimization problem that underlies this association is combinatorial NP-hard and the difficulty of solving it is aggravated in HetNets by the disparity of transmit powers and BS computational capabilities. To find close-to- optimal user-to-BS associations, we develop a two- phase method based on semidefinite relaxation (SDR) with randomization, which is a powerful method for solving a class of combinatorial problems. Unlike the majority of other polynomial complexity techniques, SDR has a provable approximation accuracy. Numerical examples show that, in comparison with other user-to-BS association approaches, the one proposed herein enables more efficient utilization of resources and a significantly higher number of users to be accommodated.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".