Information geometric approach to channel identification: a comparison with EM-MCMC algorithm
Bibliographic record
Abstract
After reviewing the information geometric channel identification algorithm (IGID) (A. Zia et al., 2003), the application of the algorithm for semi-blind identification of the MIMO channel with Gaussian input sources is discussed. The method is developed based on the results from information geometry; specifically, the alternating projections theorem first proved by Csiszar and G. Tusnady (1984) which provides an iterative method for minimizing the distance between two sets of probability distributions. Also, an EM-type identification algorithm (EM-MCMC) for which the necessary expectation computations are performed using Markov-chain Monte-Carlo (MCMC) method is introduced. The comparative analysis of channel identification using two methods for MIMO systems with ISI-free flat-fading channels is given. It is shown that the IGID method has a similar performance while benefiting from an analytical solution. Thus, complex multidimensional integrations usually necessary in similar EM-type methods are avoided. This characteristic provides very fast computation times relative to previous EM-type algorithms.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".