On Performance of MIMO-OFDM/TDM Using MMSE-FDE with Nonlinear HPA in a Multipath Fading Channel
Bibliographic record
Abstract
Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) combined with time division multiplexing (OFDM/TDM) based on frequency domain equalization (FDE) has been proposed to reduce the high peak-to-average power ratio (PAPR) of OFDM and improve the bit error rate (BER) performance in comparison to the conventional OFDM. However, due to the nonlinearity of the high-power amplifier (HPA) at the transmitter and the fact that the PAPR problem is not completely eliminated, the nonlinear noise due to HPA saturation still degrades the BER performance. In this paper, we theoretically evaluate the effect of nonlinear HPA on the performance of MIMO-OFDM/TDM using a minimum-mean square-error frequency-domain equalizer (MMSE-FDE). We determine the equalization weights while taking into account the negative effect of HPA saturation and then evaluate the system performance in terms of average BER and ergodic capacity by way of both, numerical and computer simulation. Our simulation results have shown that appropriate system design can make MIMO-OFDM/TDM more robust against nonlinear degradation due to HPA saturation in comparison to MIMO-OFDM while reducing required signal-to-noise ratio (SNR) for the given target BER.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".