WLC42-6: Non-Linear Precoding for OFDM Systems in Spatially-Correlated Frequency-Selective Fading MIMO Channels
Bibliographic record
Abstract
This paper presents non-linear precoding design in closed-loop multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) over spatially- correlated, frequency-selective fading channels. Our analysis takes into consideration receiver channel mismatch due to imperfect channel estimates, and transmitter channel mismatch due to estimation errors, channel variations over feedback delay and feedback noise. We present a general spatially-correlated, frequency-selective fading MIMO channel model and derive the conditional means of the channel response. Exploiting the channel statistics, which are only available at the receiver, we design new non-linear zero-forcing (ZF) Tomlinson-Harashima precoding (THP) for uncoded MIMO OFDM. The channel statistics do not need to be sent back to the transmitter, which avoids the possible maximum-Doppler-shift transmitter mismatch. Our proposed precoders are robust against time variations, channel estimation errors and antenna correlations, and offer a significant system performance gain over conventional THP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".