Network modulation: A new dimension to enhance wireless network performance
Bibliographic record
Abstract
We introduce an approach called network modulation which gives us a new dimension to improve wireless network throughput and save energy. In current wireless systems, when a source transmits data to the receiver through a single-hop or multi-hop wireless path, the physical layer modulates and demodulates the information bits hop-by-hop, and the transmission over each hop is treated the same as in a point-to-point communication link. Given the broadcast nature of wireless medium and the wide variation of wireless channel quality, we let a sender transmit messages to multiple receivers simultaneously, using a software mapping technology, called network modulation, to redefine the constellation of typical quadrature amplitude modulation (QAM) schemes. As the software-based network modulation schemes do not require specialized communication hardware, they can be implemented with low cost and high flexibility. Network modulation can be used to improve network performance in a wide range of scenarios, for anycast (broadcast, multicast and unicast) services, one-way or two-way traffic, and single-hop or multi-hop wireless paths, in infrastructure or ad hoc networks. The minimum requirement for applying network modulation is that there are no less than three nodes within each others' transmission ranges, so we can consider modulation, topology control, resource allocation, and routing jointly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".