Uplink Load Balancing over Multipath Heterogeneous Wireless Networks
Bibliographic record
Abstract
The rapidly growing traffic demand on mobile networks and the new services offered by service providers requires effective strategies for handling network resources. One such strategy is the ability of mobile devices to be attached to multiple radio nodes simultaneously. By this strategy the average data rate, that a mobile terminal can transmit/receive, increases and improves the reliability. This new strategy enables the use of dynamic load balancing among radio nodes, which in turn protects mobile networks from congestion caused by sudden load increase or poor channel conditions. Nevertheless load balancing requires techniques for efficiently splitting traffic without increasing the delay or causing packet reordering. In this paper we focus our study to the uplink load balancing case. We propose QBALAN, a new load balancing algorithm that uses two main strategies: long-term strategy splits traffic that remains on the system for long periods of time, and the short-term strategy splits traffic such that packet reordering is minimized. Numerical results show that our algorithm reduces the splitting error while reducing packet reordering.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".