Error-Rate Analysis for Bit-Loaded Coded MIMO-OFDM
Bibliographic record
Abstract
Bit-loaded orthogonal frequency-division multiplexing (OFDM) with convolutional coding is a powerful technique for transmission over quasi-static frequency-selective fading channels. Further enhancements in data rate are achieved by combining loaded OFDM with multiple-input-multiple-output (MIMO) transmission. Motivated by the lack of appropriate error-rate analysis techniques for this popular type of transmission system, in this paper, we develop a novel analytical method for bit-error-rate (BER) and frame-error-rate (FER) estimation of bit-loaded coded OFDM and MIMO-OFDM systems using singular value decomposition (SVD), operating over frequency-selective quasi-static channels with nonideal interleaving. Then, we introduce three different applications of the proposed analysis. First, we compare the performance of several OFDM bit-loading schemes and propose a hybrid loading scheme that selects the best loading for each channel realization from a number of candidates. Second, we introduce three adaptive interleaving schemes: 1) selecting the best interleaver from a number of predefined interleavers; 2) a novel adaptive bit-interleaving algorithm based on the pairwise error probability; and 3) a spatial interleaving scheme for MIMO-OFDM-SVD systems with separate information sources. Third, we introduce an adaptive coded-modulation algorithm by using our BER and FER estimation techniques.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".