Low-complexity QoS-aware frequency provisioning in downlink multi-user multicarrier systems
Bibliographic record
Abstract
This paper studies quality-of-service (QoS)-aware frequency provisioning schemes for a downlink multi-user multi-carrier system in a frequency-selective fading environment with diverse user-QoS requirements in terms of target delay and effective capacity (EC). Since a jointly optimal power and sub-carrier allocation requires an exponential-time exhaustive search, we explore an alternative simpler approach with two steps: (i) frequency provisioning to allocate the available subcarriers to the demanding users, followed by (ii) power allocation for the set of subcarriers assigned to each user. The single-user EC-based power allocation can be directly applied to step (ii). Furthermore, its results can also be used to develop a low-complexity knowledge-based frequency provisioning algorithm for step (i). The proposed iterative frequency provisioning algorithm starts with an initial rate-based guess and analyzes the incremental cost of power with respect to each user's QoS requirement to modify the allocated numbers of subcarriers that can further reduce the power consumption, if possible, in each subsequent iteration. Illustrative results show that the initial rate-based guess can be very effective in many situations, whereas, when the demanding users have similar required ECs but widespread delay requirements, the initial rate-based guess can be far from the optimum solution and therefore, more iterations are needed.
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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 | 0.003 |
| 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".