Call Admission on the Uplink and Downlink of a CDMA System Based on Total Received and Transmitted Powers
Bibliographic record
Abstract
We consider the problem of call admission and resource management in a code-division multiple-access (CDMA) wireless network supporting several types of services over a range of transmission rates and offering possibly different grades of service. Resource requirements are considered separately for the uplink and downlink. The high-level objective is to design a simple admission scheme that ensures adequate signal-to-interference ratios for both the incoming call (if accepted), as well as previously admitted calls. Our approach is based on two key ideas: 1) an integrated measure of resource utilization that is agnostic to the details of the traffic mix and 2) an estimate of the additional resources required to accommodate the new call seeking admission. The current work considers estimation of the total received power distribution on the uplink and the total transmitted power distribution on the downlink, and prediction of their displacements as a result of admitting a new call. The total received/transmitted power distributions are estimated based on data obtained from the power control module. The displacement of total received/transmitted power is predicted based on the characteristics of the incoming call and the current resource utilization. Dynamic call capacities are compared with static capacities to indicate the effectiveness of the proposed algorithm in achieving high network utilization with low probability of overload.
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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 | 0.007 |
| 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 | 0.002 | 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".