On the effect of neural network compensation on MIMO‐STBC systems in the presence of HPA nonlinearity
Bibliographic record
Abstract
Abstract In this paper, we focus on the effect of nonlinear high‐power amplifiers (HPA) on the multiple‐input‐multiple‐output space‐time block coded (MIMO‐STBC) systems. In order to compensate the HPA nonlinearity, we propose a new receiver scheme based on a neural network algorithm in conjunction with the maximal‐ratio combining (MRC) technique. The performances of the proposed nonlinear network (NLN), called NLN‐MRC receiver, are evaluated for a MIMO‐STBC systems over uncorrelated Rayleigh fading channels. Analytic expressions of the average symbol error rate and the error vector magnitude are delivered. We also analyse the channel capacity of the considered system assuming the perfect knowledge of the channel coefficients and the use of the water‐filling approach. Simulation results show that the proposed compensation technique can efficiently reduce the effect of HPA distortions. In addition, we note an excellent agreement between analytic expressions and Monte‐Carlo simulation curves. Furthermore, the proposed adaptive NLN‐MRC scheme has a low complexity, fast convergence, and best performance than its competitors given in the literature. Copyright © 2014 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".