Bibliographic record
Abstract
Radio resource management (RRM) plays an extremely important role in efficient utilization of the limited radio resources to provide guaranteed quality of service (QoS). Power and transmission rate cotrol are considered as two primary RRM components in mobile cellular systems. The former method is well-known for upholding required signal quality and reducing the energy consumption, while the latter is used to maximize the system throughput. In this thesis, the algorithm of simultaneously adapting transmission power and data rate to maximize system throughput and minimize the power consumption for W-CDMA system is proposed [1]. The greedy rate packing (GRP) allocation scheme is applied in the rate adaptation, where the higher transmission rate is assigned to the users with better channel conditions meanwhile mminimizing the transmission power. It can be interpreted that more resources, such as power and transmission rate, are allocated at time instants when channel conditons are favourable. As a result, the resource allocation will be working in an efficient way. Another problem in power control is that using fixed stepsize power control (FSPC) can not fully react to the changing of the fading fluctuations when mobile speed changes. A novel dynamic stepsize power control (DSPC) algorithm [2] is presented as well, so as to enhance the system performance. The performance of the proposal algorithms are evaluated through computer simulations, and compared with the traditional approaches from the literature. We model and stmulate all major components of the system within an accurate frame/time slot structure specified in the UMTS W-CDMA system. Numerical results verify that significant performance improvement can be achieved with the proposal algorithms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".