Dropped-Packets Repair and Recovery for Delay-Tolerant Wireless Systems Using Retroactive Interference Cancellation
Bibliographic record
Abstract
Data download and storage over wireless networks is a popular application for various multimedia such as images, audio, and video files. In such applications, the end-user may listen to or watch the downloaded media in real-time, and/or will playback the downloaded file multiple times in the future. Consequently, improving the quality of the stored multimedia will be highly desirable by the end-user, even if the initial real-time version had less quality. Therefore, this work proposes a novel technique to improve the throughput of wireless networks that adopt nonorthogonal transmission and automatic repeat request (ARQ). Unlike conventional systems, the proposed scheme stores the dropped data packets for further offline processing where some packets can be recovered by applying successive interfer-ence cancellation (SIC) in a retroactive manner. Consequently, the proposed packet repair and recovery (PRR) scheme can improve the reliability of the received data without additional packet retransmission, which may offer a significant throughput gain. To evaluate the effectiveness of the proposed scheme, it is applied to image transmission for transportation systems. The obtained results confirm that a considerable portion of the dropped packets can be successfully recovered and the visual quality of the received image can be improved. In particular scenarios, the obtained results show that the proposed PRR technique managed to reduce the packet drop rate (PDR) from 10^{−1} to 10^{−3}, i.e., 100 fold, which implies that 99% of the packets that were initially dropped were successfully recovered.
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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) | 0.000 | 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) | 0.001 | 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".