MMSE‐based iterative processing with imperfect channel and parity check in MIMO systems
Bibliographic record
Abstract
It is known that the acquisition of the complete channel state information at receivers is difficult in multiple‐input multiple‐output (MIMO) systems. Channel estimation error is unavoidable in practical applications. Under imperfect channel conditions, the channel estimate is directly applied to the equalisation process in the conventional minimum mean‐square error (MMSE)‐based turbo equalisation scheme. A few studies treat the channel estimation error as an independent component from the channel estimate and slightly enhanced performance is achieved. Unlike the existing work, the authors derive the MMSE‐based iterative processing conditioned on channel estimate. Moreover, they note that in low‐density parity check coded systems, the parity‐check procedure is also involved. The pass in parity check indicates that the message bitstream is successfully recovered. This information can be utilised to reduce the overall computational complexity by degrading the MIMO size since the unknown parameters are reduced. By extending the analysis in a small‐scale MIMO system to a large‐scale one, they propose to utilise the normalised transmission power in the development. Numerical results show the proposed schemes outperform the existing schemes in terms of system bit error rate and computational complexity performance.
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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 | 0.000 | 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 | 0.000 |
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".