Improved V-BLAST symbol detection using short block codes
Bibliographic record
Abstract
We present a new iterative symbol detection and decoding scheme for coded V-BLAST architectures (ISDD-BLAST). In this scheme, V-BLAST blocks are spatially encoded using a short block code. Using a Tanner graph representation of the code's parity-check matrix, as each symbol is detected, the detector is able to determine when an error has occurred. The detector then uses a modified bit-flipping algorithm to flip the least reliable bit, then greedily returns to the symbol changed, and continue with the detection sequence. When the greedy algorithm is permitted to reach up to a maximum of 1000 symbol detections, an 8times8, 8-PSK V-BLAST system using the proposed detection scheme shows an Eb=N0gain of about 7dB over an equivalently uncoded system. As ISDD-BLAST greedily searches for symbols until such time as a codeword is found, its average complexity at mid to high SNR values is only slightly greater than the original V-BLAST.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".