Efficient symbol detection in Multi-Device STBC-MIMO System
Bibliographic record
Abstract
In this paper, we present an iterative algorithm for detecting symbols at the receiver side in a multi-device (MD) STBC-MIMO System. We find that this algorithm has good performance especially at a low SNR region in rank-deficient MIMO systems. A multi-device system is a good example of a rank deficient system because the total number of transmit antennas are greater than the number of receive antennas. The detection problem is known to be NP-Hard. Exhaustive search for finding an optimal detection (maximum likelihood (ML) detection) has a computational complexity that increases exponentially with the number of mobile devices, transmit antennas per mobile device, and the number of bits per symbols. We apply estimation of distribution algorithm (EDA) to detecting symbols in a rank-deficient MD-STBC-MIMO system. The proposed EDA detector finds a nearly optimal solution in real time and also requires very low amount of computation as compared to the ML (exhaustive search) and sphere decoder (SD). The effectiveness of EDA is verified through simulation results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".