Boosting the Throughput of HARQ with Off-the-Shelf Codes
Bibliographic record
Abstract
In this work, we propose and optimize a new coding strategy designed to enhance the throughput of hybrid ARQ (HARQ) transmissions over i.i.d. block-fading channels where the channel state information (CSI) is unknown at the transmitter. Unlike in the conventional approach, where many blocks are exclusively assigned to carry the HARQ rounds of a single packet, we use a joint coding and the same channel block is logically shared among many packets. To reduce the complexity, we use a two-layer coding, where, first, packets are mixed on the binary level, and the resulting mixture is then passed for the conventional channel encoder. This allows us to decode packets on a block-by-block basis. Knowing the decoder error rate curves, the challenge is to find the optimal packets mixing that maximizes the total throughput. We show how to solve this problem using a dynamic programming (DP) approach, which we next apply to the off-the-shelf turbo-codes. We also discuss how the parameters of the practical turbo-encoder may be modified to take advantage of the proposed HARQ scheme. In numerical examples, our scheme is compared to the conventional incremental redundancy HARQ (IR-HARQ), and it yields a notable gain of 1 - 2dB in the region of high throughput, where IR-HARQ fails to provide any improvement.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".