Precoding for Multiuser Orthogonal Space-Time Block-Coded OFDM: Mean or Covariance Feedback?
Bibliographic record
Abstract
This work presents precoding design for error-rate improvement in closed-loop multiuser orthogonal space-time block-coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) downlink, where both mean feedback and covariance feedback are available. We derive adaptive linear precoding and nonlinear Tomlinson-Harashima precoding (THP) over a transmit-antenna-correlated, frequency-selective fading MIMO channel with estimation errors and feedback delay. In our precoder, mean-feedback precoding or covariance-feedback precoding, is adaptively chosen at the user terminal. The maximum achievable signal-to-noise power ratio (SNR) is used as the precoding-mode selection criterion. Each user calculates the selection metric and decides whether mean feedback is necessary. We confirm the intuition that mean-feedback precoding offers BER gains over covariance-feedback precoding when mean feedback becomes sufficiently accurate. Our adaptive precoding outperforms either mean-feedback precoding or covariance-feedback precoding in multiuser OSTBC OFDM, and considerably reduces the bit error rate (BER). Non-linear adaptive precoding is shown to outperform linear adaptive precoding.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".