Distributed Precoding for OFDM in Two-Way Relaying Communications
Bibliographic record
Abstract
This paper is concerned with the design of distributed precoding in the multiple access (MA) phase for two-way relaying communication (TWRC) systems using OFDM. The error probability analysis is conducted to establish the diversity and coding gains for three error types in the MA phase. Then, the design criteria of distributed precoding to achieve the maximum diversity and coding gains are given. The frequency-grouped linear constellation precoding (F-GLCP) is first investigated and shown not to be able to achieve the maximum diversity gain under type-3 errors. A novel frequency-time GLCP (FT-GLCP) that performs precoding in both the frequency and time domains is then proposed. It is proved that the proposed FT-GLCP is able to achieve the maximum diversity gain under type-3 errors while maintaining the maximum diversity and coding gains under type-1 and type-2 errors. To corroborate the theoretical analysis, simulation results are provided to show the advantage of the proposed FT-GLCP over other schemes in both Rayleigh and Rician fading channels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".