Widely-Linear Processing of Faster-than-Nyquist Signaling in the Presence of IQ Imbalance
Bibliographic record
Abstract
Faster-than-Nyquist (FTN) signaling is a promising approach to increase the spectral efficiency (SE) of next-generation wireless communication systems. In this paper, we investigate the detection of FTN signaling in the presence of in-phase and quadrature (IQ) imbalance in frequency-selective fading channels. We show that IQ imbalance at the transmitter and the receiver of FTN signaling results in an image of the transmit and the received signal, respectively, and this image represents an additional interference. We use concepts from widely linear processing to exploit the correlation between the received signal and its complex conjugate. In particular, we propose a widely-linear minimum mean square error (WL-MMSE) algorithm to estimate the transmit FTN signaling in the presence of IQ imbalance and frequency-selective channels. We additionally prove that the mean square error (MSE) of the proposed WL-MMSE is small than its counterpart of the linear-MMSE (L-MMSE). Simulation results verify our findings in terms of bit error rate (BER) and MSE performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".