Capacity of iteratively estimated channels using LMMSE estimators
Bibliographic record
Abstract
This paper explores how iterative channel estimation, symbol detection and decoding at the receiver affects the achievable capacity compared with a non-iterative, purely pilots-based scheme. First, a bound is put on the linear minimum mean-square error (LMMSE) estimator of doubly selective radio channels. Subsequently, a bound on the capacity using an LMMSE estimator is found. These bounds take into account the uncertainty in symbol detection on channel estimation, and incorporate the effect of channel estimation error on channel capacity. The paper describes how bounds can be calculated for typical multipath and fast fading radio channels. The interaction between the symbol detector and the decoder is depicted by exploiting an extrinsic information transfer (EXIT) chart, where a bound on the detector curve is found. With optimal LMMSE pilot-based channel estimation, the bounds demonstrate that iterative channel estimation has little advantage except at fading rates greater than 1% of the symbol rate. To make the results more concrete, finite order modulations are also investigated and compared with the case of Gaussian distributed symbols, showing that the latter can be used to approximate the performance of the former in a more convenient way.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".