Iterative Channel Estimation for Large Scale MIMO with Highly Quantized Measurements in 5G
Bibliographic record
Abstract
Large-scale MIMO systems offer high spectral efficiency with excellent error performance at low power so long as accurate channel estimates are available. When channel estimation is performed using only pilot signals, undesirably long pilot sequences are needed to achieve the required accuracy. This paper describes an iterative receiver algorithm where detected/decoded data symbols extend the pilot sequences as virtual pilot signals. By using extrinsic feedback, where only information on how the error correction code decoder modifies a posteriori bit probabilities from the detector output is fed back to the channel estimation and detection system, the errors made by the detector and channel estimator do not lead to instability. The proposed system is able to estimate time domain multipath channels with high accuracy. Communications with this system only requires 0.5 dB more power than the system using ideal channel state information, and about 2.5 dB less power than the system that estimates the channel using only the pilot signal. The receiver is also able to operate with coarsely quantized measurements so that low cost receivers can be used at each antenna.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".