OFDM systems with CPM mappers for smart grid applications
Bibliographic record
Abstract
Smart grid can be thought of as a network of interconnected wireless networks. In such a network, critical reports generated at the customer sites are required to be communicated to a control station through several layers of wireless networks. Communication in these sub-networks is typically achieved using IEEE 802.11 technology that uses Orthogonal Frequency Division Multiplexing (OFDM). Conventionally, in an OFDM system BPSK, QPSK and QAM mappers are used. In this paper, Continuous Phase Modulation (CPM) mapper in an OFDM system is proposed: i) to enhance the physical layer reliability through the introduction of correlation among the transmitted OFDM symbols; and ii) to reduce the Peak-to-Average Power Ratio (PAPR) of transmitted OFDM symbols. The objective, over here is to examine the PAPR performance of OFDM systems with CPM mappers with and without Selective Mapping (SLM) technique. Simulations show that CPM mapper in OFDM systems can provide better PAPR performance compared to conventional mappers used in the existing IEEE 802.11 standards. It is noted that CPM mapper in OFDM is very effective in reducing PAPR and can be easily adopted in 802.11 standards for wireless network communications in smart grid.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".