A New Link Scheduling Algorithm for 60 GHz-WPAN Communication System
Bibliographic record
Abstract
With apparent advantages of several GHz license-free spectra, low power consumption, low-cost implementation of CMOS devices, and so forth, 60 GHz wireless communication technology becomes the first choice for short-range high rate wireless communication in future wireless personal area network (WPAN) and wireless local area network (WLAN). While different from common WPAN system with omnidirectional communication, 60 GHz-WPAN system generally adopts directional communication. In this paper, a device (DEV) interconnected mechanism of 60 GHz-WPAN based on switched beamforming technology is structured firstly. Without considering the directional transmission characteristic of 60 GHz communication, most of the 60 GHz-WPAN Medium Access Control (MAC) protocols fail to make full use of the high spatial reuse degree caused by directional communication. In this paper, an average time slot multiplexing allocation (ATSMA) algorithm based on space/time division multiple access (STDMA) solution is proposed, which allows concurrent transmission in a single time slot. The designed link schedule process includes common channel interference (CCI) probing scheme, link coexistence determination scheme, and link schedule algorithm. Extensive simulations are conducted in order to demonstrate the efficiency of proposed scheme. Simulation results demonstrated that the capacity gain is obvious using ATSMA algorithm, no matter whether under omnidirectional antenna or under directional antenna.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".