Network Coded Multi-Source Cooperative Communication in BICM-OFDM Networks
Bibliographic record
Abstract
In this paper, we study a cooperative diversity scheme for wireless systems employing network coding and the combination of bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM). The considered system comprises multiple sources, one relay, and one destination. The relay decodes the signal received from all sources and performs network coding before forwarding the signal to the destination. We propose a simple cooperative maximum-ratio combining scheme for the destination which can successfully exploit the full spatial and frequency diversity offered by the channel for arbitrary numbers of sources and arbitrary linear modulation schemes. Furthermore, we propose techniques to reduce the signaling overhead and the decoding complexity at the destination. To gain insight for system design, we derive a closed-form upper bound for the asymptotic worst-case pairwise error probability and the diversity gain of the considered network coded cooperative BICM-OFDM system. These analytical results reveal the influence of various system parameters, including the number of sources, the free distance of the code, and the frequency diversity of the involved links, on performance. Based on the derived analytical results, we develop schemes for optimal relay placement and power allocation. Simulation results corroborate the derived analytical results and confirm the effectiveness of the developed optimization framework.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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 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".