EXIT-Aided Scheduled Iterative MIMO Detection Under Non-Homogeneous Antenna Propagation Gain Scenarios
Bibliographic record
Abstract
The non-homogeneous antenna propagation gain, potentially caused by the diverse large-scale fading effects in wireless communication channels, has a significant impact on the reliabilities of multiple-input multiple-out (MIMO) systems. We propose to utilize extrinsic information transfer (EXIT) to analyze the convergence characteristic of the factor graph (FG) based iterative MIMO detection mechanisms in an effective way. Based on the EXIT analysis, we propose a low-complexity scheduled algorithm for FG-based iterative MIMO detection, which speeds up the convergence of the mutual information exchange between the variable nodes and the observation nodes. To address the complexity issue in 5G new radio (NR) systems, where low-density parity check (LDPC) codes are adopted for data transmission, we also extend the proposed algorithm to concatenated detection and decoding in MIMO-LDPC systems to achieve low complexity. Simulation results show that the convergence speed of MIMO detection can be improved by at most 50%, while for the MIMO-LDPC system, the proposed algorithm can achieve 2 dB gain compared to the conventional minimum mean square error (MMSE) detection mechanism.
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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 | 0.000 |
| 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 | 0.000 |
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".