An efficient wireless resource management scheme to support handoff data recovery in packet-switched cellular multicast networks
Bibliographic record
Abstract
To support data transfer reliability similar to that of a fixed multicast network, migrating terminals in a packet-switched cellular wireless multicast network supporting reliable multicast data transfer need to recover lost data during handoffs before they can merge into the respective multicast groups in the new cells. The multicast groups in packet-switched wireless networks typically share resources on a statistical multiplexed basis. To minimize impact on other terminals, this paper proposes to allow part of a multicast group's assigned bandwidth to be shared by the handoff terminals for transient data recovery using the proposed Weighted Fair Share (WFS) method with optimal weight selection. Handoff terminals are admitted into the new cell using the proposed Multicast Connection Admission Control (MCAC) scheme. These methods together constitute the Fair and Efficient Wireless Multicast resource management Scheme (FEWMS) presented in this paper. Under FEWMS, a migrating terminal can quickly recover lost data and merge into the existing multicast group during a handoff. Simulations using self-similar traffic sources show that the proposed method reduces the handoff failure probability of migrating terminals and the average packet delay of the multicast group, and increases the overall system throughput, compared with an existing proposal. Evaluations of different performance measures show that the system throughput does not give a complete picture of the system performance, as different resource management schemes may have substantial impact on other performance measures such as average delay and handoff failure probability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".