QoS guarantee and power distribution for soft handoff connections in cellular CDMA downlinks
Bibliographic record
Abstract
A two-phase power distribution scheme for supporting quality-of-service (QoS) and best effort traffic is proposed. We first formulate the power distribution for QoS traffic as an optimization problem so that the number of simultaneously transmitting connections is maximized. Optimum power distribution is difficult to implement in practice due to both the computational complexity and the requirement for global information about the mobile station (MS) locations, connection channel conditions, and traffic load in the system. We then propose a heuristic scheme of power distribution for soft handoff (SHO) connections. The full scheme includes an initial power distribution (IPD) and a power distribution adjustment (PDA). IPD allocates BS power resource based on the channel condition of each individual connection, while PDA further coordinates the power distribution between neighboring base stations (BSs) in order to accommodate more connections. The proposed power distribution scheme can achieve a capacity close to that of the optimum power distribution, while providing much higher transmission throughput for best effort data traffic. The proposed power distribution scheme can be applied to existing SHO schemes for efficient BS power resource usage. The scheme does not require global information, and its implementation can be further simplified by performing IPD only with slight performance degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".