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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".