Power Allocation for Practicable Capacity Maximization in Eigen-MIMO
Bibliographic record
Abstract
Water-filled eigenchannels offer the highest MIMO information-theoretic capacity. However, in practice there are many factors, such as digital modulations instead of Gaussian signals, finite block lengths, and imperfect power allocation, that combine to degrade the capacity from the Shannon limit to the practicable capacity - the uncoded throughput - of a digital link. Complexity reduction is also an important factor for a practical system. In this paper, we address the problem of power allocation for maximizing the practicable capacity in eigen-MIMO with the total input power constraint and perfect channel state information (CSI) at the transmitter. We use non-adaptive modulation at the transmitter in order to avoid the high complexity in both the electronics and the protocol - of adaptive modulation. The optimum power allocation reveals new and interesting capacity behavior, and it turns out to be different than that of the water-filling.
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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 | 0.000 |
| 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".