Low-complexity iterative detection and decoding in finite geometry LDPC-coded MIMO systems
Bibliographic record
Abstract
This paper presents a low-complexity iterative joint detection and decoding algorithm for finite geometry-low density parity check (FG-LDPC) coded multiple-input multiple-output (MIMO) systems, in which the MIMO channel detector and the FG-LDPC decoder iteratively exchange soft information. The key to the simplicity of the algorithm is the use of a bit flipping-based decoder for FG-LDPC code. An important issue addressed here is the generation of soft information from the binary outputs of the bit-flipping decoder to be fed back to the MIMO detector. Simulation results show that the proposed joint detection and decoding algorithm achieves a substantial reduction in decoding error probability compared to a cascaded detector and a decoder. We also compare the performance with belief-propagation (BP) based detector-decoders which are significantly more complex. The new algorithm provides a practical approach to joint detection and decoding of popular FG-LDPC codes in a MIMO system, a task which is computationally unmanageable with a BP algorithm.
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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 | 0.004 |
| 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) | 0.001 | 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".