Capacity maximisation in eigen-multiple-input multiple-output using adaptive modulation and Reed–Solomon coding
Bibliographic record
Abstract
Eigen-multiple-input multiple-output (MIMO) with water-filling gives the maximum information-theoretic capacity, but realising this maximisation is not straightforward because practical factors are omitted. Adaptive modulation and error coding are combined for finding the maximum practicable capacity (throughput of correctly detected bits) in eigen-MIMO. Quadrature amplitude modulation (QAM) is the logical choice of modulation and here Reed–Solomon (RS) coding is used. RS coding has the advantages of algorithmic simplicity, low-memory requirements, and decoder complexity, and its unique closed-form error probability makes it possible to obtain an optimal power allocation, signal constellation size(s), and code rate(s) on the eigenchannels, for the maximum practicable capacity. The proposed adaptive scheme is applied to two architectures for the encoders/decoders (CODECs): outer coding, where a single CODEC is deployed for the overall serial data; and inner coding, where there is a CODEC for each eigenchannel. The optimal power allocation is different to the water-filling used for the information-theoretic capacity. Also, simple and accurate approximations are found for the bit-error rate (BER) of a single Rayleigh channel and of a 2×2 system. Finally, a selection procedure between different system configurations is presented for obtaining the highest practicable capacity subject to either average or instantaneous output BER.
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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 | 0.001 | 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".