XOR-Assisted Cooperative Diversity in OFDMA Wireless Networks: Optimization Framework and Approximation Algorithms
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Bibliographic record
Abstract
Network coding has been leveraged with cooperative diversity to improve performance in single channel wireless networks. However, it is not clear how network coding based cooperative diversity can be exploited effectively in multi-channel networks where overhearing is not readily available. Moreover, the question of how to practically realize the promising gains available, including multi-user diversity, cooperative diversity and network coding in multi-channel networks, also remains unexplored. This work represents the first attempt to unravel these two questions. In this paper, we propose XOR-CD, a novel XOR-assisted cooperative diversity scheme in OFDMA wireless networks. It can greatly improve the relay efficiency by over 100% mostly, thus uplifting the throughput performance by over 30% compared to conventional cooperative diversity scheme. In addition, we formulate a unifying optimization framework that jointly considers relay assignment, relay strategy selection, channel assignment and power allocation to reap different forms of gains. We design efficient polynomial time algorithms to solve the NP-hard problem with provably the best approximation factor, and verify their effectiveness using realistic simulations.
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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 | 0.001 |
| Open science | 0.000 | 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 it