Channel Equalization for Multi-Antenna FBMC/OQAM Receivers
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Bibliographic record
Abstract
In this paper, the problem of channel equalization in filter bank multicarrier (FBMC) transmission based on the offset quadrature-amplitude modulation (OQAM) subcarrier modulation is addressed. Finite impulse response (FIR) per-subchannel equalizers are derived based on the frequency sampling (FS) approach, both for the single-input multiple-output (SIMO) receive diversity and the multiple-input multiple-output (MIMO) spatially multiplexed FBMC/OQAM systems. The FS design consists of computing the equalizer in the frequency domain at a number of frequency points within a subchannel bandwidth, and based on this, the coefficients of subcarrier-wise equalizers are derived. We evaluate the error rate performance and computational complexity of the proposed scheme for both antenna configurations and compare them with the SIMO/MIMO OFDM equalizers. The results obtained confirm the effectiveness of the proposed technique with channels that exhibit significant frequency selectivity at the subchannel level and show a performance comparable with the optimum minimum mean-square-error equalizer, despite a significantly lower computational complexity. The possibility of tolerating significant subchannel frequency selectivity gives more freedom in the multicarrier system parameterization. For example, it is possible to use significantly wider subcarrier spacing than what is feasible in OFDM, thus relieving various critical design constraints.
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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 | 0.001 | 0.001 |
| 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 it