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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".