CAAHR: Content aware adaptive HARQ retransmission scheme for 4G/LTE network
Bibliographic record
Abstract
A drastic increase in traffic of mobile broadband is seen in the past few years, which is further accelerated by the increased usage of smart phones and its applications. The availability of good smart phones and better data connectivity are encouraging mobile users to use video services. This huge increase in usage will pose a lot of challenges to the wireless networks. The existing wireless networks lack content awareness within an application. In this paper, we present an approach to content awareness at the Hybrid Automatic Repeat reQuest (HARQ) level of Long Term Evolution (LTE) system by changing the retransmission count based on the importance of packet being scheduled. This scheme improves the received video quality by up to 8% for users under poor channel conditions with negligible increase in packet delay and Physical Downlink Shared Channel (PDSCH) utilization factor. The main benefits of this scheme are its simplicity and ease of implementation without compromising on performance of the network elements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".