LDPC coded two-way MIMO relay networks with physical layer network coding
Bibliographic record
Abstract
Low-density parity-check (LDPC) codes offer a very powerful error correction technique which allows data transmission in wireless networks with arbitrarily low probability of error. This paper studies LDPC-coded wireless networks with a two-way relay scheme. Two techniques have been developed to increase the throughput of relay networks. First, only two time slots are used to perform one round of information exchange, by exploiting the concept of physical layer network coding. The sources transmit simultaneously their independent messages to the relay in slot 1, while the relay broadcasts the processed signal to the sources in slot 2. Secondly, to reduce the processing time at the relay, we have proposed an estimate-and-forward strategy for LDPC-coded two-way relay networks, where no LDPC decoder is required at the relay. The proposed estimate-and-forward strategy simplifies the processing at the relay, especially when the sources change the encoding parameters. Finally, an effective decoding algorithm has been developed at each source, by subtracting self-interference from the received signals. The simulation results indicate that the proposed LDPC-coded relaying network achieves extremely reliable transmission at very low signal-to-noise ratios.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".