Distributed scheduling and power control for cognitive spatial-reuse TDMA networks
Bibliographic record
Abstract
We investigate the problem of distributed scheduling and power control for vertical spectrum sharing in spatial-reuse time division multiple access (STDMA) networks. The objective is to minimize the transmission length (in term of time slots) of secondary users (e.g. users in femtocell networks) subject to the interference-limit constraint for primary users (e.g. users in cellular networks) and quality-of-service (QoS) guarantee of secondary users. This problem is known to be NP-complete. We therefore propose a novel distributed two-stage algorithm based on the distributed column generation method to find the near-optimal solution for the transmission schedule. In the first stage, the dual problem corresponding to the transmission length minimization problem subject to the minimum bandwidth requirement of secondary users, called the restricted master problem, is solved to obtain a dual optimal solution at each secondary transmitter. The dual optimal variables are passed to the second stage to solve the pricing problem. The pricing problem here finds a feasible channel access pattern such that the sum of dual optimal variables is greater than 1 subject to the interference constraints for primary users and the signal-to-interference-plus-noise ratio (SINR) constraints for secondary users so that the solution of the master restricted problem can be improved. We also develop a distributed algorithm for solving the pricing problem based on local measurement at each secondary transmitter and a limited number of message exchanges. The proposed algorithm is compared with previously proposed methods and is evaluated in terms of the schedule length and the number of message exchanges.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".