Reducing Complexity Using Path Selection for Sequential Blind Beamforming for Wireless Communications
Bibliographic record
Abstract
In this paper, a path selection technique based on power level detection is introduced to our previously proposed MRC-sequential blind beamforming method to reduce the receiver complexity. MRC-SBB mitigates the inter-symbol and intra-symbol interferences by recovering the signal and its integer and non-integer multiple replicas using jointly CMA, LMS and adaptive fractional time-delay-estimation filtering. While the resulting improvement using this method is proportional to the number of the detected paths and also the interpolation filters' complexity, targeting portable applications motivates us to propose a technique to reduce the power consumption while maintaining good performance. Thus, in this paper, a path selection technique based on the paths' power-level detection and digital filter dynamic activation is introduced to significantly reduce the power consumption of the hardware circuits. Simulations in different scenarios using path selection for MRC-SBB were carried out, and the obtained results validate the power consumption efficiency without sacrificing the BER performance.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".