Pre‐LNA smart soft antenna selection for MIMO spatial multiplexing/diversity system when amplifier/sky noise dominates
Bibliographic record
Abstract
Abstract Due to a beamforming capability, Soft Antenna Selection (SAS) techniques have shown great performance improvements over the traditional antenna sub‐set selection schemes for Multi‐Input and Multi‐Output (MIMO) communication systems. A SAS method is basically defined by a pre‐processing matrix which is located in RF domain, either before or after the Low Noise Amplifier (LNA). In this paper, we propose a pre‐LNA SAS module which is adaptively tuned to maximise the mutual information or the Signal to Noise Ratio (SNR) gain for spatial multiplexing or diversity transmissions, respectively. The SAS optimality is discussed for the two practical cases where either the sky noise or the amplifier noise dominates. We analytically show that the pre‐LNA SAS method even outperforms the full‐complexity MIMO system for when the number of receive Radio Frequency (RF) chains is greater than or equal to the number of the transmit RF chains. The simulation results verify this claim and also superiority of the proposed scheme to the post‐LNA SAS. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".