Topology-Aware Modulation and Error-Correction Coding for Cooperative Networks
Bibliographic record
Abstract
User cooperation in wireless networks is inherently a cross-layer optimization problem. We identify a new direction for cooperative communications: i.e., in addition to the point-to-point communication channel between the transmitter and the receiver, the communication configuration should take the network topology into account. In this paper, we first propose a network modulation (NM) design that can transmit bits with different SNR requirements in a single symbol transmission. We then propose an error-correction coding assisted relay (EAR) scheme that is also configured according to the network topology. We study the performance of NM and EAR in both a three-node collinear network and a two-dimensional cellular network. Extensive simulations have been conducted, which demonstrate the substantial performance gain of the proposed schemes, in terms of both a higher network throughput and a lower bit-energy consumption. Comparing between NM and EAR, NM is more beneficial for the downlink performance and EAR is more beneficial for the uplink performance. Combining NM and EAR leads to a more efficient cooperative network. It is concluded that the topology-aware physical layer design will be a promising direction with many open issues for further study.
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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 | 0.001 | 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".