Mean Mutual Information Per Coded Bit Based Precoding in MIMO-OFDM Systems
Bibliographic record
Abstract
This work proposes a per-subband multiple input multiple output (MIMO) precoder selection technique for point-to-point MIMO orthogonal frequency division multiplexing (OFDM) based bit interleave coded modulation (BICM) systems with the soft-output minimum mean square error (MMSE) receiver. Given a pre-designed precoder codebook, the codeword/precoder that maximizes the mean of the mutual information per coded bit (MMIB) on all subcarriers within a subband is selected. The main advantages of this technique are the following: i) the precoder selection metric is explicitly related to BICM performance, thus it outperforms the previously proposed precoding techniques; ii) with commonly used unitary precoding codebooks, this technique works for an arbitrary number of transmit streams unlike the minimum singular value based method which does not work when the number of input streams is the same as the number of transmit antennas; iii) when multiple packets are transmitted and one precoder is used for these transmitted packets, an algorithm that combines the MMIB of each packet is proposed using an upper bound on the average packet error rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".