Adaptive modulation and decision feedback equalization for frequency‐selective MIMO channels
Bibliographic record
Abstract
SUMMARY In this paper, an adaptive modulation scheme for the multiple‐input multiple‐output (MIMO) frequency‐selective channels is investigated. We consider a scenario with precoded block‐based transceivers over spatially correlated Rayleigh multipath MIMO channels. To eliminate the inter‐block interference, the zero‐padding is used. The receiver is equipped with a MIMO minimum‐mean‐squared‐error decision feedback equalizer. The precoder aims to force each subchannel to have an identical signal‐to‐interference‐plus‐noise ratio (SINR). To adjust the constellation size, the unbiased mean square error at the equalizer output is sent back to the transmitter. To simplify our analysis, the feedback channel is considered as instantaneous and error free. We first derive the probability density function of the overall SINR for flat fading and frequency‐selective channels. On the basis of the probability density function of the upper bound of the SINR, we evaluate the system performance. We present accurate closed‐form expressions of the average spectral efficiency, the average bit error rate and the outage probability. The derived expressions are compared with Monte Carlo simulation results. Furthermore, we analyze the effect of the channel spatial correlation. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Open science | 0.001 | 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".