Mitigation of PA Nonlinearity for IEEE 802.11ah Power-Efficient Uplink via Iterative Subcarrier Regularization
Bibliographic record
Abstract
An orthogonal frequency division multiplexing (OFDM) transmitter for 802.11ah uplink may consume unnecessarily high power due to the malicious effect of a large peak-to-average power ratio (PAPR). This is particularly a problem for client devices (CDs) under the low-power wide-area (LPWA) technology in an Internet of thing (IoT) system, where high PAPR signals may drive the power amplifier (PA) to operate with large input back-off (IBO). This article focuses on receiver-side signal compensation (SC) techniques and introduces a novel scheme called iterative subcarrier regularization (ISR), which is based on the generalization of Papoulis-Gerchberg algorithm (GPGA). We claim that the proposed scheme is completely compatible with 802.11ah as it only exploits the prior information available in the standard operations and popular system-build-in functions in the iterative signal reconstruction process. Extensive numerical evaluations demonstrate that the proposed scheme can improve PA efficiency by 4-9 dB for uplink signaling.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".