Dynamic Scheduling in High Speed Downlink Packet Access Networks: Heuristic Approach
Bibliographic record
Abstract
In this paper, a heuristic approach to build a near-optimal dynamic code allocation policy for the High-Speed Downlink Packet Access (HSDPA) downlink scheduler is presented. The approach is developed based on information about the structure of the optimal policy for the two-user case and then generalized (using results from order theory) to support any number of users. To find the optimal code allocation policy, an MDP-based dynamic programming model for the HSDPA downlink scheduler was developed. The model then solved using value iteration for the two-user case. The computation complexity of this model grows exponentially with the buffer size of each user and even more rapidly with the number of users. On the other hand, the proposed heuristic approach has linear computation complexity and can be extended to any finite number of users with any finite buffer size. Simulation is used to compare the performance of this policy to the performance of the optimal policy. The obtained results show that the heuristic policy performs very close to the optimal one with huge reduction in the computation time.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".