An Iterative Decision Feedback Algorithm using the Cholesky Update for OFDM with Fast Fading
Bibliographic record
Abstract
Orthogonal frequency-division multiplexing (OFDM) is an efficient technique for high data rate transmission in wireless communications. If the channel varies within the time duration of an OFDM symbol, intercarrier interference (ICI) creates, resulting in a degradation of the error performance. Minimum mean square error decision feedback estimation (MMSE-DFE) algorithms can combat ICI. A standard MMSE symbol detection requires a calculation of the inverse of the signal covariance matrix which has a high computational cost. For this reason, alternative MMSE techniques have been developed which use recursive calculations and decision feedback to compute symbol estimates at a low computational cost with minimal increases in error performance. However, these computations are ill-conditioned and their error performance degrades when low precision arithmetic is used. This paper proposes a new iterative MMSE-DFE algorithm with the use of Cholesky updates. This method is more stable than other iterative MMSE-DFE algorithms. Simulation results show that the method improves the performance with no increase in computational complexity.
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.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".