Closed BER of STBC-MIMO-OFDM systems over nonlinear frequency selective channel and cancellation technique for HPA distortion
Bibliographic record
Abstract
Multiple-input multiple-output (MIMO) technology and orthogonal frequency-division multiplexing (OFDM) can be combined to design a robust communications scheme with increased spectral efficiency and system capacity. To reach high power efficiency, MIMO-OFDM systems are equipped with high-power amplifiers (HPA). When it operates near its saturation region, the HPA causes nonlinear distortions. Unfortunately, the nonlinearity of HPA has a crucial impact on the global system performance. In this paper, we focus on the effect of the HPA nonlinearity on the space-time block coded (STBC) MIMO-OFDM systems. Analytic expression of the average Bit error rate (BER) is delivered running under frequency selective channel. We also note an excellent agreement between analytic expressions and Monte Carlo simulation curves. In addition, this work introduces a new iterative cancelation technique for the nonlinearly detected by the receiver. The new proposed approach is based on the Bussgang theorem and consists in iterative estimation and annulation of the nonlinear distortion caused by the high power amplifier of the transmitter.
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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 | 0.001 | 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) | 0.004 | 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".