Blind ML JADE in Multipath Environments Using Differential Evolution
Bibliographic record
Abstract
In this paper, we tackle the problem of joint angles and time delays estimation (JADE) in a non-data aided (NDA) scenario where no pilot symbols are available at the receiver. A differential evolution (DE) technique is proposed in the context of maximum likelihood (ML) estimation is proposed to solve the resulting multi-dimensional optimization problem. DE is a metaheuristic global optimization algorithm-based on population, that finds the optimum iteratively by trying to improve a candidate solution based on an evolutionary process. We introduce the improved DE using a pseudo-pdf for easier generation of individuals. Simulations results show that the proposed solution is significantly more efficient in terms of global convergence than the classic differential evolution algorithm (CDEA) as well in terms of RMSE. Moreover, due to a very useful approximation, we are able to reduce even further the computational complexity of the proposed technique without any significant performance loss. Computer simulations also show the distinct advantage of the new NDA-DE approach over the existing techniques. Most remarkably, it also approaches the Cramér-Rao lower bound (CRLB) at medium and high SNR levels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".