Recursive least squares algorithm for blind deconvolution of channels with cyclostationary inputs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The authors propose a new discrete time blind deconvolution technique for linear channels driven by cyclostationary inputs. This problem arises in digital communications, seismic signal processing, and many other applications. In particular, homomorphic approaches are applied to the cyclic autocorrelation of the fractionally-spaced sampled output of the channel. First, the method identifies the differential cepstrum parameters of the complex channel by means of a recursive least squares (RLS) algorithm. The RLS algorithm is based on the special characteristics of a cyclic autocorrelation matrix and an appropriate matrix inversion lemma. Once the differential cepstrum parameters are recovered, then the impulse response of the channel/equalizer is obtained by simple recursive formulas. Only partial information is required, i.e., the cyclic period and the distribution of the input data. It is shown that the method can directly identify the characteristics of either the channel or its inverse, provided that an unknown channel satisfies a special condition. The method is evaluated by means of computer simulations and is found to perform efficiently.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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