Calibration for single-carrier preFDE transceivers based on property mapping principles
Bibliographic record
Abstract
In general, the unequal RF circuitry in the transmit and receive chains at the base station (BS) prevents the exploitation of the uplink (UL) channel estimate for proper pre-equalization in time division duplex (TDD) systems. To avoid additional transceiver hardware costs for matching networks, the idea of relative calibration was introduced to cope with the different effective channel impulse responses of UL and downlink (DL) by means of signal processing. However, multiple-input multiple-output (MIMO) transmission in frequency-selective channels does not allow for classical frequency-domain calibration principles based on total least squares (TLS) approaches. Consequently, more complex structured total least squares (STLS) problems must be solved. In this paper the application of the signal property mapping principle is introduced to iteratively solve the STLS calibration problem. Exemplified by simulation results for single-carrier frequency-domain pre-equalization (SC-preFDE) systems the algorithm indicates good and fast convergence behavior and effectively exploits noisy UL and DL channel measurements for system calibration.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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 | 0.001 |
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".