Code-Aided Direction Finding in Turbo-Coded Square-QAM Transmissions
Bibliographic record
Abstract
We investigate the problem of direction of arrival (DOA) estimation from turbo-coded square-QAM- modulated signals. We propose a new code-aided (CA) maximum likelihood (ML) direction finding technique that exploits the soft information obtained from the soft-input soft-output (SISO) decoder in the form of log-likelihood ratios (LLRs). Unlike standard estimation techniques, the proposed method improves the system performance by appropriately embedding the direction finding and receive beamforming tasks within the turbo iteration loop. In fact, the DOA estimates and the soft information are iteratively exchanged between the decoding and estimation blocks, respectively, according to the so called-turbo principle. Simulation results show that the new CA DOA estimation scheme lies between the two extreme direction finding schemes: completely non-data aided (NDA) and data-aided (DA) estimations. Moreover, the new CA DOA estimator reaches the corresponding CA Cramér-Rao lower bounds (CRLBs), over a wide range of practical SNRs thereby confirming its statistical efficiency in practice. The proposed scheme can be applied to systems, as well, when they are decoded with the turbo principle.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".