Novel method for communications using orthogonal division duplexing of signals (ODD)
Bibliographic record
Abstract
As mobile radio systems expand their role in global communications, there is a need for both increased efficiency and an ability to operate in a variety of arbitrary unpaired spectrum assignments. While traditional mobile systems are mostly operated with separated transmit and receive spectrum assignments, these assignments are becoming more difficult to find for new systems among the crowded radio spectrum. Developing a radio system that is suitable for an unpaired spectrum assignment and that can also provide rapid availability of channel information for adaptive transmissions is thus a significant goal. The paper introduces a novel concept, which we refer to as orthogonal division duplexing (ODD), to address these issues. The ODD technique interleaves multiple orthogonal carriers in the same band for transmission and reception. These orthogonal carriers are produced using techniques similar to those used for OFDM. Using the same channel for both transmission and reception enables the channel conditions to be rapidly and accurately estimated by each transceiver, eliminating the delays or errors associated with traditional channel reporting techniques. The ODD transmission system is particularly well suited to multimedia high-speed mobile communications and may be designed to be adaptable to asymmetric traffic flows.
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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 | 0.001 | 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) | 0.003 | 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".