Interference management using basestation coordination in broadband wireless access networks
Bibliographic record
Abstract
Abstract This paper proposes a transmission‐scheduling algorithm for interference management in broadband wireless access networks. The algorithm aims to minimize the cochannel interference using basestation coordination while still maintaining the other quality of service (QoS) requirements such as packet delay, throughput and packet loss. The interference reduction is achieved by avoiding (or minimizing) concurrent transmission of potential dominant interferers. Dynamic slot allocation based on traffic information in other cells/sectors is employed. In order to implement the algorithm in a distributed manner, basestations (BSs) have to exchange traffic information. Both real‐time and non‐real‐time services are considered in this work. Results show that significant reduction in the packet error rate can be achieved without increasing the packet delay at low to medium loading values and with a higher but acceptable packet delay at high loading values. Since ARQ schemes can also be used for packet error rate reduction, we compare the performance of the proposed scheme with that of ARQ. Results indicate that although ARQ is more effective in reducing packet error rate, the proposed algorithm incurs much less packet delay particularly at medium to high loading. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".