A network linear block coding approach to selective detect-and-forward multi-way relaying
Bibliographic record
Abstract
In this work, we introduce a network linear block coding framework for multi-way relaying with differential MPSK modulation.We consider a system with K user terminals and L relays employing a selective detect-and-forward (DF) relaying protocol.Each relay is associated with a relevant group of terminals.During the first K phases, each terminal broadcasts its own signal to relay nodes and all the other terminals.During the following L phases, each relay forwards a linearly combined signal to all the terminals only if all the symbols from its relevant group were detected successfully.Such a system can be represented as a linear block code in systematic form, where the transmissions over direct links provide the information symbols and the relays form the parity check symbols.Therefore, the decoding at each terminal consists of decoding a (K + L, K) linear block code.We first analyse the theoretical performance of our system with optimal decoding, including pairwise error probability, codeword error probability and bit error rate.It is shown that our system can achieve a diversity order equals to the minimum Hamming distance of the equivalent code when using maximum likelihood decoding.For practical implementation, a sub-optimal decoder based on the log-domain belief propagation First of all, I would like to express my sincere gratitude and respect to my supervisor, Professor Harry Leib, for his inspiring guidance and continuous support during my graduate study.I have learned a lot from his broad knowledge, rigorous attitude and enthusiasm for research.Without him, this thesis could not be completed.I am
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".