Bit-interleaved coded modulation with iterative decoding (BICM-ID) using signal space diversity (SSD) over non-orthogonal amplify-and-forward (NAF) relay channels
Bibliographic record
Abstract
Diversity is a powerful technique to improve the reliability of digital communications systems over wireless channels. In this thesis, the error performance and precoder design of a bandwidth-efficient bit-interleaved coded modulation with iterative decoding (BICM-ID) system over a non-orthogonal amplify-and-forward (NAF) half-duplex single-relay channel is investigated to effectively combine cooperative, time and modulation diversities. At first, a tight union bound on the asymptotic bit error probability (BEP) of the NAF-BICM-ID system employing signal space diversity (SSD) via a precoder is derived for an arbitrary cooperative block length of 2N. This bound provides a useful tool to predict the error performance without the need of time-consuming simulations. A design criterion that characterizes the effect of the precoder to the performance is then developed, which gives an insight on how to obtain a good precoder to fully exploit diversity. Attention is then paid to the NAF-BICM-ID system using a 2x2 precoder, where a closed-form expression of the bound is obtained. Based on this expression, the optimal class of 2x2 precoders is developed. Different from the optimal precoders designed for uncoded systems, the derived precoder indicates that the source terminal only needs to send the superposition of the first symbol and the rotated version of the second one in the first time slot, while being silent in the remaining slot to achieve the best asymptotic performance. For a good convergence property, it is further shown that a rotation angle that maximizes the minimum Euclidean distance of the superposition constellation should be used. Optimal rotation angles are then analytically determined for various modulation schemes. The usefulness of the proposed precoder is verified by both the error bound and simulation results. Finally, based on extrinsic information transfer (EXIT) charts, both the convergence property and the asymptotic performance of the system
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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) | 0.000 | 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 | 0.000 | 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".