MIMO soft demodulation via semidefinite relaxation: Efficient dual-scaling implementation and performance analysis
Bibliographic record
Abstract
For MIMO systems with binary or QPSK signalling, soft demodulation techniques based on semidefinite relaxation (SDR) offer a desirable balance between performance and computational cost. In this paper we expand on an extension of that approach to systems with higher-order QAM signalling. In particular, we develop a customized dual-scaling algorithm to solve the semidefinite program that constitutes the core computational task of the demodulator, and we evaluate its performance via simulations and using an EXIT chart analysis. In comparison to several state-of-the-art demodulators, the proposed demodulator provides performance that comes close to that of the list sequential (LISS) demodulator, at a computational cost that is close to that of the minimum mean square error (parallel) soft interference cancellation (MMSE-SIC). Furthermore, the distribution of the computational cost is concentrated around its mean.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".