Capacity of SPRAS-MIMO System with MMSE Channel Estimation
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Bibliographic record
Abstract
Multiple input multiple output (MIMO) systems equipped with smart passive receive antenna selection (SPRAS) technique need the channel state information at receiver side (CSIR) to optimally and adaptively provide the same performance as full-complexity MIMO structure. However, a perfect CSIR is not usually available due to channel estimation errors. In this paper, we investigate the effect of minimum mean square error (MMSE) channel estimation on the capacity of SPRAS-MIMO system. The lower and upper bounds on the mutual information under channel estimation error are studied and the mutual information is maximized with the optimum passive beamformer. Numerical results show how the capacity decreases by increasing the channel estimation error. The effect of the channel estimation error on the bit-error-rate (BER) performance is also studied.
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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 it