A red-based discard strategy for unacknowledged mode RLC in LTE
Bibliographic record
Abstract
In order to satisfy the increasing demands of wireless mobile communications, Long Term Evolution (LTE) has been proposed as an all-IP-based network to bring higher peak data rates and better spectral efficiency. As one of the sub-layers of LTE, Radio Link Control (RLC) layer is likely to congest or overflow due to handover or undesirable channel condition. In this paper, based on Random Early Detection (RED), a discard strategy is proposed for Unacknowledged Mode (UM) RLC to overcome the congestion and buffer overflow problems. Taking LTE systems into account, a simpler improved RED discard strategy is proposed, which is suitable to the delay-sensitive stream that transmitted by a UM RLC entity. This discard strategy can be employed in UE for uplink or evolved NodeBs (eNB) for downlink. The delay and discard performances of the proposed discard strategy are evaluated by both computer simulations and theoretical analysis. According to the simulation results, some suggestions about configuring parameters of the proposed discard strategy are given. In the end, to systematize the strategy and make it more practical, a cross-layer specific proposal is proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".