Optimum Power Allocation for Maximum-Likelihood Channel Estimation in Space-Time Coded MIMO Systems
Bibliographic record
Abstract
This paper presents an optimum power allocation strategy for the maximum likelihood based channel estimation in the space-time coded multiple-input multiple-output systems employing a data-bearing approach for pilot-embedding. The corresponding channel estimation error, the Chernoff's upper bound on the detection error probability, and a lower bound on channel capacity of such systems are analyzed. Based on such analysis, the relationship between these two bounds are revealed, then a unified optimum power allocation scheme is proposed based on jointly optimizing both bounds subject to an acceptable channel estimation error. Simulation results indicate that the proposed power allocation scheme yields a better performance in terms of the error probability, whereas the equal power allocation scheme can be reasonably used as a suboptimum approach with an acceptable performance degradation. Furthermore, the unequal power allocation with more power constantly allocated to the data part yields a much better performance than the one with more power constantly allocated to the pilot part
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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".