An Optimization Framework for XOR-Assisted Cooperative Relaying in Cellular Networks
Bibliographic record
Abstract
This work seeks to address two questions in cooperative OFDMA networks: First, how network coding based cooperative diversity can be exploited effectively when overhearing is not readily available. Second, how to realize various forms of gains available, including multi-user diversity, cooperative diversity, and network coding. The main contribution of this paper is an unifying network utility maximization framework that jointly considers relay assignment, relay strategy selection, channel assignment and power allocation. We formulate the optimization problem both with and without XOR-CD, a simple XOR-assisted cooperative diversity scheme. We show that the optimization of physical layer resource allocation with XOR-CD is equivalent to a weighted 3-set packing problem, which is NP-complete, and can be efficiently solved with provably the best approximation factor. Without XOR-CD, the problem reduces to a weighted bipartite matching problem which can be optimally solved.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".