Adaptive Reed-Solomon Coding in Eigen-MIMO with Non-Adaptive Modulation
Bibliographic record
Abstract
An adaptive coding scheme for spectral efficiency improvement in eigen-MIMO is presented. It uses Reed-Solomon (RS) codes with non-adaptive QAM. Non-adaptive modulation is of interest since it reduces the high complexity - in both the electronics and the protocol - required for deploying the more commonly treated adaptive modulation. RS codes have practical advantages in terms of their algorithmic simplicity, memory requirements and decoder complexity. Unlike many codes, an analytical solution for the error probability is available for RS coding. This facilitates finding the jointly optimal code rate(s) and power allocation on the eigenchannels, with the criterion of maximum instantaneous practicable capacity (data throughput). The adaptation is applied to two different architectures of the encoders/decoders (CODECs) for eigen-MIMO, and their performances are compared with the uncoded case. The outer coding architecture refers to a single CODEC working on the overall serial data, and inner coding refers to separate CODECs for different eigenchannels. The adaptive RS system with optimum power allocation reveals new capacity behavior which is different to that of water-filling. For the moderate values of SNR (6-20 dB) typical of wireless systems, the improvement in the capacity over the no-coding case is greater for the inner coding architecture.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".