Adaptive Coded Cooperation in Wireless Networks
Bibliographic record
Abstract
User cooperation enables single antenna terminals to benefit from spatial diversity by partnering with other users to create a virtual transmit antenna array. A promising form of cooperation is called coded cooperation which integrates cooperation with channel coding, showing great performance gains. However, the optimal degree of cooperation between the users changes with the channel conditions and furthermore, there is no known expressions indicating the required degree of cooperation for given channel conditions. This paper proposes an adaptive protocol based on incremental redundancy using an ARQ/FEC scheme with rate-compatible punctured convolutional codes (RCPC). By employing a ACK/NACK feedback channel from the partner, each user incrementally decreases its coding rate in the first cooperation phase in order to increase its chances of being correctly decoded by the partner and thus benefit from spatial diversity, while preserving as much resources as possible to provide relaying diversity for the other user. Simulation results illustrate the gains and flexibility of our protocol for both reciprocal and non-reciprocal channels. The results show that our adaptive protocol outperforms for all channel conditions coded cooperation with a fixed degree of cooperation with gains on the order of 2 to 4 dB.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".