On the Achievable Sum Rates of Iterative MIMO Receivers with Linear Front-Ends
Bibliographic record
Abstract
In this paper, the rate achievable in Multiple-Input-Multiple-Output (MIMO) systems with iterative receivers based on linear front-end (FE) processing is investigated. First, the communication with Gaussian input signal is assumed and the Extrinsic Information Transfer (EXIT) chart is applied to evaluate the achievable sum rate. Then, the method for deriving the EXIT chart for a more practical case has been introduced. As a specific model, which fits the real turbo receivers better, communication with large size uniform constellations is discussed where the information being exchanged between the receiver's iterative block are Log-Likelihood-Ratios (LLRs) of the transmitted bits. It is shown that, in this situation, the iterative process does not improve the performance from achievable sum rate point of view in both high and low SNR regimes. However, the iterative process is shown to help in the medium SNR range, which is the range of interest.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".