Covariance precoding schemes for MIMO OFDM over transmit‐antenna and path‐correlated channels
Bibliographic record
Abstract
Abstract This paper considers covariance‐feedback based linear precoding (LP) and nonlinear Tomlinson–Harashima precoding (THP) for multiple‐input multiple‐output (MIMO) orthogonal frequency‐division multiplexing (OFDM) systems. Orthogonal space–time block coded (OSTBC) ‡ OFDM and spatially‐multiplexed (SM) § OFDM are analysed. The main objective is to design precoders to mitigate the impact of transmit‐antenna and path correlations. The impact of path correlations on the pairwise error probability (PEP) of MIMO OFDM is also analysed. Closed‐form, waterfilling‐based covariance precoders are derived to minimize the worst case PEP in OSTBC OFDM. An adaptive transmission strategy is also developed for switching between precoded SM OFDM and precoded OSTBC OFDM. The switching criterion is the minimum Euclidean distance of the received codebook. The switching decision sent to the transmitter requires one feedback bit per subcarrier. The proposed precoders considerably reduce the error rate in antenna and path‐correlated channels; nonlinear precoders perform better than linear precoders. We show that the adaptive strategy can achieve full diversity gain, and it outperforms either SM or OSTBC applied individually in terms of the bit error rate (BER). Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".